DIAGNOSIS TAILORING OF HEALTH AND DISEASE

Abstract

The present invention relates generally and specifically to computerized devices capable of diagnosis tailoring for an individual, and capable of controlling effectors to deliver therapy or enhance performance also tailored to an individual. The invention integrates sensors which sense signals from measurable body systems together with external machines, to form adaptive digital networks over time of general health and health of specific body functions. The invention has applications in sleep and wakefulness, sleep-disordered breathing, other breathing disturbances, memory and cognition, monitoring and response to obesity or heart failure, monitoring and response to other conditions, and general enhancement of performance.

Claims

1. A method of improving breathing health of an individual, the method comprising: detecting a plurality of signals from one or more sensors, the plurality of signals containing at least one breath component that is associated with breathing of the individual at a plurality of points in time; filtering from the plurality of signals one or more signals of the plurality of signals or one or more components of the plurality of signals that are not associated with breathing; detecting normal and abnormal breaths from the plurality of signals as filtered via comparisons against breath events for the individual, comparisons against breath events for one or more other individuals, and known indices of health; creating, using a computing device, a composite representation that comprises creation of an index of breathing health tailored to the individual by mathematically weighting the individual's normal and abnormal breaths via scoring of (i) quantitative indices of physical health symptoms of the individual, wherein the quantitative indices of physical health symptoms comprise components of physical health symptoms and related scores of one or more of STOP-BANG questionnaire, Epworth Sleepiness Scale, quality of life survey, symptom survey, and Functional Outcomes of Sleep Questionnaire and (ii) quantitative indices of physical examination findings of the individual, wherein the quantitative indices of physical examination findings comprise components of physical examination findings and related scores of one or more of STOP-BANG questionnaire and Berlin questionnaire; and treating the breathing health of the individual based on the composite representation by delivering one or more effector signals to control one or more body functions associated with the breathing health of the individual.

2. The method of claim 1, wherein the index of breathing health is predetermined or dynamic.

3. The method of claim 2, wherein the index of breathing health is tailored dynamically for the individual based upon one or more of recorded patterns in the individual, recorded patterns in one or more other individuals, patient history, population database, population characteristics, machine learning, and disease type.

4. The method of claim 1, wherein a sensor of the one or more sensors is physically in contact with the individual.

5. The method of claim 1, wherein a sensor of the one or more sensors is not physically in contact with the individual.

6. The method of claim 1, wherein a signal of the plurality of signals is a biological signal.

7. The method of claim 1, wherein a signal of the plurality of signals is a non-biological signal.

8. The method of claim 1, wherein the plurality of points in time comprises one or more days for repeated testing.

9. The method of claim 6, wherein the biological signal is selected from one or more of sounds from an airway associated with breathing, sounds detectable on a surface of the individual associated with breathing, vibrations detectable on the surface of the individual associated with breathing, chest wall movement associated with breathing, abdominal movement associated with breathing, heart rate patterns associated with breathing, alterations in heart output associated with breathing, levels of oxygenation of the individual associated with breathing, chemistry levels of the individual associated with breathing, galvanic skin resistance associated with breathing, brain function associated with breathing, and levels of color of the individual associated with breathing.

10. The method of claim 1, wherein the plurality of signals is selected from one or more levels of pressure associated with breathing, one or more levels of ambient sound associated with breathing, one or more levels of vibration associated with breathing, one or more levels of temperature associated with breathing, and one or more levels of gas composition associated with breathing, and combinations thereof.

11. The method of claim 1, wherein the quantitative indices of physical health symptoms of the individual comprise one or more measures of central nervous system, peripheral nervous system, cardiovascular system, respiratory system, skeletal muscles, and skin.

12. The method of claim 1, wherein the quantitative indices of physical examination findings of the individual measure one or more of the central nervous system, peripheral nervous system, cardiovascular system, respiratory system, skeletal muscles, and skin by physical measurements of the individual through sensed signals.

13. The method of claim 1, wherein the plurality of signals comprise signals having breath-related components and non-breath related components.

14. The method of claim 13, wherein the breath-related components comprise one or more of cough, snore, wheeze, and component associated with a normal breath.

15. The method of claim 13, wherein the non-breath related components comprise one or more of apnea and signal noise.

16. The method of claim 3, wherein the index of breathing health is dynamic and varies based on one or more of the plurality of signals detected from the individual over time, changes in the health symptoms over time, changes in the physical examination findings over time, and one or more disease states.

17. The method of claim 1, wherein the mathematical weighting is fixed.

18. The method of claim 1, wherein the mathematical weighting is variable.

19. The method of claim 1, wherein the mathematical weighting is selected from spectral methods, stochastic methods, correlation methods, calculus based approaches, geometric based approaches, and combinations thereof.

20. The method of claim 1, wherein mathematical weighting comprises an enciphered functional network represented by symbolic code.

21. The method of claim 20, wherein the symbolic code is a cypher.

22. The method of claim 1, wherein the method comprises performing iterative analysis when the individual is at times of low breathing health and when the individual is at times of high breathing health.

23. The method of claim 1, wherein the method comprises performing statistical correlation between signals acquired from the individual and those stored in a database.

24. The method of claim 23, wherein the database represents signals from the individual over time, signals from one or more different individuals, or a database from multiple individuals.

25. The method of claim 1, wherein the representation is displayed using one or more of a consumer device, a medical device, a computer, and a printed representation.

26. A system to improve breathing health of an individual, the system comprising: a processor; and a memory storing instructions that, when executed by the processor, cause the processor to perform operations comprising: detecting a plurality of signals from one or more sensors, the plurality of signals containing at least one breath component that is associated with breathing of the individual at a plurality of points in time; filtering from the plurality of signals one or more signals of the plurality of signals or one or more components of the plurality of signals that are not associated with breathing; detecting normal and abnormal breaths from the plurality of signals as filtered via comparisons against breath events for the individual, comparisons against breath events for one or more other individuals, and known indices of health; creating a composite representation that comprises creation of an index of breathing health tailored to the individual by mathematically weighting the individual's normal and abnormal breaths via scoring of (i) quantitative indices of physical health symptoms of the individual, wherein the quantitative indices of physical health symptoms comprise components of physical health symptoms and related scores of one or more of STOP-BANG questionnaire, Epworth Sleepiness Scale, quality of life survey, symptom survey, and Functional Outcomes of Sleep Questionnaire and (ii) quantitative indices of physical examination findings of the individual, wherein the quantitative indices of physical examination findings comprise components of physical examination findings and related scores of one or more of STOP-BANG questionnaire and Berlin questionnaire; and treating the breathing health of the individual based on the composite representation by delivering one or more effector signals to control one or more body functions associated with the breathing health of the individual.

Description

BRIEF DESCRIPTION OF THE DRAWINGS

[0153] Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:

[0154] FIG. 1 shows a schematic representation of the invention, including biological sensors or external sensors, a signal processing unit and a computing device that can form a representation of bodily functions, e.g., an “enciphered functional network”. A control unit can be used to treat abnormal physiological functions via a device or biological organ (“effector”) tailored by measuring response to therapy in a feedback loop.

[0155] FIG. 2 illustrates the invention for one preferred embodiment of breathing health, with functional domain(s) of lung function represented by sensed signatures that can be tracked over time including breath sounds, chest wall movement, movement of the body using sensors in a bed or chair, changes in oxygenation. The enciphered functional network (with analysis engine) combines this analytical system with effector group(s).

[0156] FIG. 3 shows a flowchart illustrating how the enciphered functional network represents a bodily function in an individual person, for one preferred embodiment of breathing health, as functional domains represented by sensed signatures. Sensed signatures are analyzed by algorithms that match signature patterns to desired and undesired behavior, to databases (e.g. analyzed using statistical correlation) in a network of “population behavior” or historical behavior of that individual, to monitor function, guide and assess response to therapy.

[0157] FIG. 4 shows an example of sensed signatures for a preferred embodiment of breathing health, for functional domains representing nervous system and non-nervous system functions and tasks. The array of sensed signatures becomes the measured representation of that bodily function for that individual person over time.

[0158] FIG. 5 shows the task of modifying bodily function using the enciphered network of the invention, here for one preferred embodiment of breathing health. Modification is tailored to the individual via personalized sensory signatures and machine learning in the enciphered network. Modification includes therapy, such as for sleep-disordered breathing, but can also enhance normal function for that individual. Modification operates in a continuous feedback, assessing response via the enciphered network to prevent excessive or deleterious modification.

[0159] FIG. 6 shows illustrative body locations for sensed signatures and modifying various functional domains. Sensor locations are indicated by open (white) regions and effector (modifying) regions by filled (black) regions. Their relative size varies in each individual, is determined by machine learning for each individual and is not portrayed to scale.

[0160] FIG. 6B shows an illustrative framework for the Enciphered Network. Arrays of sensors or effects connect the invention with the individual person. The processing network links the sensor or effector arrays with health states using logic which can be machine learning, rule-based, heuristic-based, database lookup or other associations.

[0161] FIG. 7 shows examples of sensors in this invention, which may comprise a sensor element, power source, microprocessor element, nonvolatile storage and communication element. Several types of sensor element are illustrated, such as photodetector (for skin temperature, metabolic light sensing, drug sensing), galvanometer (for skin impedance), pressure (for weight, skin breakdown), temperature or chemical. The invention can also use external sensors (FIGS. 1, 12-18) that provide a variety of extrinsic or artificial signatures (FIGS. 12-18).

[0162] FIG. 7A illustrates consumer sensors that can provide sensed signals for the invention to manage health and disease. This includes a smartphone, which can provide sensed signals of breath sounds (used in one preferred embodiment for breathing health), movement, heart rate and other signals. Other consumer devices include a smartwatch, motion sensor in the house, motion sensor in a bed, chair or automobile or plane seat, consumer microphone, light detector, and weighing scales.

[0163] FIG. 7B shows the invention flowchart for managing breathing health and detecting sleep apnea using breath sounds from a smartphone alone, as one preferred embodiment.

[0164] FIG. 7C shows an example in which the invention can analyze sounds from a smartphone at distance from the individual to detect normal breaths, snoring and other disturbances. Sound analysis in this test example is validated by reference to a clinical polysomnogram (performed simultaneously with the sound recording), which verifies disturbances. In actual practice, the invention is intended to be used without a polysomnogram.

[0165] FIG. 7D illustrates the invention analyzing sounds from a smartphone at a distance from the individual to detect normal breaths, a 20 second period without breathing (apnea), followed by a loud arousal event (sound ‘disturbance’). In this test case, sound analysis is validated by reference to a clinical polysomnogram (performed simultaneously with the sound recording), which verifies disturbances. In actual practice, the invention is intended to be used without a polysomnogram.

[0166] FIG. 7E shows the specific analysis flowchart for analyzing sound files from a smartphone.

[0167] FIG. 7F shows a example in which the invention analyzes sounds from a smartphone alone at a distance from the individual, and detects snoring, periods of no breathing for >10 seconds, and other breath sounds.

[0168] FIG. 7G shows an example in which the invention analyzes sounds from a smartphone alone at a distance from the individual, and detects periods of loud snoring and other breath sounds.

[0169] FIG. 7H shows an example in which the invention analyzes sounds from a smartphone alone at a distance from the individual, and detects a period of loud snoring or disturbance/noise using the area under the sound curve.

[0170] FIG. 7I shows an example in which the invention analyzes sounds from a smartphone alone at a distance from the individual, and detects a period of noise.

[0171] FIG. 7J shows an example in which the invention analyzes sounds from a smartphone alone at a distance from the individual, and detects very low amplitude sound.

[0172] FIG. 8 shows some preferred embodiments of sensed signatures of sleep disordered breathing.

[0173] FIG. 9 shows a preferred embodiment of effectors to modulate sleep health and treat disease.

[0174] FIG. 10 shows some preferred embodiments of sensed signatures for heart failure.

[0175] FIG. 11 shows some preferred embodiments of sensed signatures of the body response to obesity.

[0176] FIG. 12 shows some preferred embodiments of sensed signatures for other conditions.

[0177] FIG. 13 shows one embodiment of an enciphered (symbolic) network to detect and treat sleep-disordered breathing.

[0178] FIG. 14 shows an embodiment of the invention to enhance body function using an enciphered network.

[0179] FIG. 15 shows cybernetic enhancement of body function using enciphered functional network.

[0180] FIG. 16 shows an embodiment of the invention to transform motor function. The flowchart shows one embodiment to enhance motor (muscle control) function of the nervous system. This is illustrated for leg muscle function, for enhancement (e.g., in military or sports use) or for medical purposes (e.g., after a stroke).

[0181] FIG. 17 shows an embodiment of the invention to enhance sensory function. The flowchart indicates embodiment for enhancing sensory perception/sensation of the nervous system. This is illustrated for alertness, for enhancement (e.g., military or sports use), for medical purposes (e.g., monitoring drowsiness or coma) or for consumer safety (e.g., identifying drowsiness while driving to control a feedback device).

[0182] FIG. 18 shows an embodiment of the invention to transform sensory function. The flowchart indicates an embodiment for transposing, or enhancing sensory perception. This is illustrated for hearing, with the invention enhancing hearing and transposing hearing function to another nervous function.

[0183] FIG. 19 shows an embodiment of the invention to create a novel “cybernetic” sensory function. The flowchart indicates an embodiment for providing a sensory function that the individual does not currently possess. This is illustrated for integrating sensation from a biosensor fora biotoxin.

[0184] FIG. 20 shows an embodiment of the invention to create a novel “cybernetic” sensory function. The flowchart indicates an embodiment for using the biological nervous system for recognition of a desired pattern.

[0185] FIG. 21 shows computer hardware for machine learning.

DETAILED DESCRIPTION

[0186] A system and method for detecting, modifying and enhancing complex functions of the body are disclosed herein. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of example embodiments. It will be evident, however, to one skilled in the art, that an example embodiment may be practiced without all of the disclosed specific details.

[0187] The invention modulates and enhances simple, complex and higher bodily functions represented in computerized fashion as a series of functional domains. In one embodiment, the function manages bodily tasks that are sensed and modulated entirely by non-medical grade devices, i.e. consumer type devices. In another embodiment, the function includes components of brain or nervous activity. A central innovation is the creation of a computerized network to represent the complex function, tailored uniquely to each individual over time. Such a representation may be called an enciphered functional network, and comprises a series of functional domains that describe normal and abnormal bodily task for that individual over time. Variations in sensed signals from the individual-normal state are interpreted by the enciphered network, as deviations, and used to guide effectors. In one preferred embodiment, the invention is applied to detect, monitor and treat sleep apnea. Other embodiments can be used to monitor and treat heart failure, manage fluid balance, manage weight to avoid obesity, or modulate alertness, mood, memory, mental performance or cognition.

[0188] FIG. 1 illustrates an example system to modify and enhance complex body functions in a human being. Specifically, the example system 100 is configured to access external signals from biological sensors 104 and from external sensors 110.

[0189] Sensors 104 can sense biological signals, from an individual, from another individual, or from a database of signals 118. The sensors 104 can be wearable on or near the body surface, reside inside the body via an orifice such as the mouth or ear, or implanted in the body.

[0190] External sensors 110 can sense biological signals, from an individual, from another individual or from a database of signals 118. Sensed signals may arise from many organ systems including the central nervous system, peripheral nervous system, cardiovascular system, pulmonary system, gastrointestinal system, genitourinary system, skin or other systems.

[0191] External sensors 110 can provide many types of signals reflecting, but not limited to, traditional physical senses including pressure/physical movement (tactile, touch sensation), temperature (thermal information, infrared sensing), chemical (galvanic skin resistance, impedance, detection of specific ions from the skin, tongue or other mucous membranes i.e. odor, taste sensation), sound (auditory sensation), electromagnetic radiation in the visible spectrum (visual sensation), movement or vibration (a measure of muscle function and balance).

[0192] External sensors 110 can also provide information on signals just outside normally sensed ranges including, but not limited to, the invisible electromagnetic spectrum (such as near-infrared light), sound waves outside the normal physiological range for humans (roughly 20 Hz to 20 kHz) but including the range sensed by animals (for instance, dogs can sense higher frequencies), chemical stimuli, drugs or toxins. In this embodiment, the invention can extend normal functioning, for instance hearing to or beyond the audible range of individuals with the greatest acuity for hearing, or restore lost function, for instance, hearing to this range in individuals with some degree of hearing loss.

[0193] External sensors 110 can provide information on signals outside of normal sensed modalities including, but not limited to, toxins such as carbon monoxide (which is a public health risk but currently non-sensed) or excessive carbon dioxide, forms of radiation (such as alpha and beta radiation, gamma radiation, X-rays, radiowaves), biotoxins such as toxins of Escherichia coli bacteria associated with food poisoning (e.g. type 0157:H7), anthrax or other agents. This embodiment of the invention would be of value for infectious disease, military and security applications.

[0194] In FIG. 1, signals are delivered either wirelessly or via connected communication to a signal processing device 114 functioning with a computing device 116 that can access an analysis database 118. The computing device 116 and signal processing device 114 communicate with a control device 120, which in turn controls a device 108 or an external device 112. The device 108 is an effector device, which can be biological or artificial. The device 108 can be wearable by the individual or in close proximity to the individual, reside inside the body via an orifice such as the mouth or ear, or implanted in the body. The computing, signal processing and control devices with sensors and effectors together form an “enciphered functional network” (EFN).

[0195] FIG. 2 summarizes the enciphered functional network (EFN) for a bodily task. The EFN may encompass one or more functional domains, each of which comprises sensors, sensed signatures for the functional domain, the analysis engine of the EFN and effector group(s) for the functional domain. At item 150 one can see the entire EFN for a particular bodily task, here illustrated for a preferred embodiment of breathing, and the functional domain termed “lung function”. Other functional domains for breathing include heart function, brain function (control of breathing centers), endocrine centers related to diurnal cycling to mention but a few. At 155 are illustrated sensors 1, 2, . . . n that are used to detect signals which together form sensed signatures 160 for this functional domain. As illustrated and discussed below, signals for lung function are diverse and include breathing sounds from a consumer or other external device, movement of the chest, movement of accessory muscles of breathing in the neck, nerve activity for these muscles (e.g. phrenic nerve, nerves in neck), airflow near the nose or mouth, oxygenation measured on the skin by optical reflectance or other means, electrical signals from the brain related to breathing or other signals.

[0196] An analysis engine 165 analyzes these sensed signatures over time to form a tailored representation of this functional domain (lung function) for an individual. Many forms of analysis can be performed as discussed below. Once the EFN has tailored this representation of lung function for the individual, signals outside of the learned ranged can be detected. For instance, in one individual reduced chest movement may indicate reduced breathing while simultaneously increased neck movement may indicate use of accessory muscles of breathing and a high probability of obstructive sleep apnea. A key feature of the invention is tailored representation, because another individual may exhibit neck movement during normal sleep which does not indicate accessory muscles of breathing, and reduced breath rate during normal sleep. Of note, the enciphered network can recruit additional sensors or stored patterns from that individual or similar individuals (such as from a database, e.g. item 118 in FIG. 1 or item 215 in FIG. 3) depending on the learned or programmed behavior of the EFN.

[0197] In item 170, the enciphered functional network includes communication with an effector group for that bodily function, which in turn signals effectors 1, 2, . . . n at step 175. In this example, effector elements may include stimulation of muscles of breathing, application of light or sound (alarm, noise) to alter sleep/wake cycling. Another key element of the invention is interconnectivity and links between each element within/with the enciphered functional network, indicated by double arrows.

[0198] FIG. 3 gives more detail on the enciphered functional network for normal or abnormal functioning of a bodily task. The list of bodily tasks addressed by this invention are broad, and each typically spans multiple physiological systems (functional domains). Bodily tasks may include but are not limited to sleep, sleep disordered breathing, cognition, mental performance, response to obesity, response to heart failure.

[0199] In FIG. 3, a preferred embodiment indicates EFN for the bodily task of breathing 210, comprising nervous system 220 and non-nervous system (non-neural) 260 networks. The networks 220, 260 comprise respective functional domains 230, 270, each defined by sensed signatures 240, 280 based on a variety of sensors. This produces nerve and non-nerve signatures for the body function, which can be normal 250 and abnormal 290—or desired 250 and undesired 290. It should be noted that the networks can interact via interactions 225 and signatures may be inter-related by expected (i.e. from physiology) or learned computational relationships 245.

[0200] The analysis engine of the enciphered functional network uses various methods including implementations of artificial intelligence (machine learning, perceptron, deep learning, autobot, and/or fuzzy logic circuits), comparison against previously stored patterns, classification schemes, expected algorithmic relationships or heuristic approaches. Rule-based systems include a database of solutions for sensed signatures, such as dermatomal distribution for shoulder nerves, fluctuations in skin reflectance indicating oxygenation, variations in auditory sound intensity that separates breathing from snoring, and normal ranges of heart rate and others familiar to one skilled in the art.

[0201] In one preferred embodiment, machine learning is accomplished via neural networks (e.g., 3 layer back-propagation networks, multi-level networks or other designs) and techniques of deep learning. Numerically, networks are defined:

[0202] (i) By node interconnects, which vary between layers of nodes (artificial neurons). Nodes are typically represented as networks, and there may be many layers and many variations in the number of nodes in input, hidden (internal) and output layers. Nodes can be connected to all nodes in layers above and below, but differential connections can also be implemented;

[0203] (ii) How nodes are connected, i.e. weights of their interconnections, which are updated in the process of learning;

[0204] (iii) A mathematical activation function, summarizing how a nodal interconnection weights input to output. Typically, the activation function of each node ƒ(x) is a composite of other functions g(x), which can in turn be expressed as a composite of other functions. A non-linear weighted sum may be used, i.e. ƒ(x)=K(Σ.sub.iw.sub.ig.sub.i(x), where K (the activation function) may be sigmoidal, hyperbolic or other function.

[0205] Various connection patterns, weighting, node activation function and updating schemes can be selected, and specific forms are optimal for different enciphered networks. The enciphered network linking EEG, cardiac and respiratory signatures to alertness, or linking weight, skin impedance, respiratory rate and cardiac output to heart failure status, for example, is more complex than a network linking recorded sound analysis with sleep disordered breathing. Recent approaches to complex tasks use recurrent neural networks, in which connections between nodes form a directed cycle to enable dynamic temporal behavior and enable complex tasks such as modeling alertness.

[0206] Alternative forms of adaptation of the enciphered network may use algorithms in the “if-then-else” formulation to link sensed signatures with defined behaviors. Several other forms of machine learning can be applied, and will be apparent to an individual skilled in the art.

[0207] An important feature of such approaches is that they do not need a priori knowledge of the specifics of human pathophysiology, but instead associate (‘learn’) patterns of sensed signatures in health (normal functioning) and deviations from these patterns in disease (abnormal functioning). They are thus well suited to complex bodily tasks that are often defined incompletely by detailed pathophysiological studies, yet still need to be monitored and treated.

[0208] The enciphered functional network can provide a computerized implementation of bedside examination by a physician—it objectively represents “good health” or “looking good”, i.e. normal skin color and blood perfusion for an individual, normal breathing for an individual, normal muscular movement for an individual and other intangible physical signs. The analysis engine of the enciphered functional network then addresses the tractable problem of identifying when sensed signals deviate from any baseline state for that individual.

[0209] The novelty of using the enciphered functional network and sensed signatures to monitor health is illustrated by the following analogy. A “high tech” approach to identifying health in an advanced hospital may find that an individual has a cardiac output of 5 l/min, normal polysomonogram with normal EEG and other parameters, normal arterial oxygen and carbon dioxide concentrations, normal cardiac nuclear stress test, and hemoglobin and other blood parameters within normal limits. A comprehensive embodiment of the current invention may come to the same conclusion through normal values of the following domains for that individual: heart (normal heart rate, normal variations with no abnormal drops in oxygen saturation during activity); lung (normal breath sounds, no wheeze, no noisy breath sounds while awake, no loud snores or apneas at night, normal oxygen saturation); general health (normal scleral color, normal diurnal temperature fluctuations, steady weight, good activity profile and normal diurnal heart rate/oxygen fluctuations). Thus, this individual appears “in good health” on bedside examination by a physician and also by this invention, which could reside on a consumer device for easy access. Thus, this invention is designed as a screening tool and ‘personal health assistant’. It is not designed to replace advanced and invasive medical examination and testing if indicated, but the device can alert the user to abnormal parameters which may accelerate referral to medical providers if needed. This could be a telehealth provider, as well as traditional provider networks. The invention thus may have value in medically underserved regions, e.g. in rural areas in the U.S. or in countries with less ready access to advanced medical care. The invention may also improve medical care by providing objective, repeatable assessment of many parameters of health tailored to that individual.

[0210] One important distinction from the prior art is that individual tailoring enables this invention to identify sensed signatures that may be normal for one individual yet abnormal for another. This invention thus advances “personalized medicine”, or “precision medicine” which are often defined at the genetic level but are often undefined for the whole individual. This invention enables robust implementation of precision health at the clinical level, based on how a function affects measureable organ systems for that individual. This clinical science is novel.

[0211] Using another analogy, the symbolic model of simple and complex tasks by the enciphered functional network may at times be akin to representing visualization by an “impressionist” painter rather than a detailed physiological representation—by one trained in the “realist” school. Again, this approach is based on the premise that in addition to the primary physiological systems required for a task, it is difficult to precisely define, secondary networked regions that become involved.

[0212] Associations of sensed signatures with normal function 250 in a patient specific range enables the invention to detect abnormal function 290 as signatures outside this range. The enciphered functional network is optimized when learning algorithms repeatedly classify interactions 255 between sensed signatures for normal 250 and abnormal 290 functions. This interconnectivity is optimal, and its complexity makes the system ideally suited for computational machine learning paradigms to modify and treat the networks 235.

[0213] In FIG. 3, a database 215 of learned representations for the individual over time, or for multiple individuals may enhance personalized diagnosis and therapy. This can be used to enhance diagnosis and therapy via the EFN for that individual.

[0214] The database 215 of learned networks (representations) between individuals is another core resource of the invention—a digital network of different sensed modalities for a function in defined populations that may be used to monitor and treat disease or improve performance. For health care or screening purposes, database 215 can be encrypted as well as de-identified, but if individual consent is obtained, e.g., in military or Institutional settings, abnormalities can be traced from or applied to specific individuals to improve their performance in the population. This forms the basis for a novel approach to crowd-sourced health or wellness screening, crowd-sourced disease monitoring, and crowd-sourced delivery of therapy.

[0215] FIG. 4 provides detail of signatures sensed 310 by the invention to represent a given bodily task tailored to an individual. The task described here for the preferred embodiment of breathing. Functional domains for the body task are broadly classified as nervous system related 315 and non nervous system related 335, which may be integrated 390. Sensed nerve signatures 315 typically represent the sensing location 320 (for instance, nerves in the neck for accessory muscles of breathing, the phrenic nerve for diaphragm activity, or sympathetic nerve firing which may indicate a stress response during sleep apnea), patterns of activity 325 (e.g., periodic with a certain frequency spectrum, or more complex and potentially represented non-linearly by fractal dimension or measures of entropy), or rate of firing 330 (e.g., the fundamental or “dominant” frequency of a spectrum or first peak on an autocorrelation function).

[0216] Numerous other nerve-related parameters are possible, e.g., nuclear scans of neuro-tissue function, e.g., MIBG scanning for autonomic ganglia, metabolic quantification using positron emission tomography based sensor information, serum levels of norepinephrine and other nerve-related signatures familiar to one skilled in the art.

[0217] Nervous and non-nervous functional domains are optimally integrated 390 for any complex bodily function, yet the distinction may be useful as embodiments utilizing nervous functional domains 315 may be implemented by electronic sensors and electronic effector devices, and form a biological neural network which can be mimicked by an artificial neural network in the enciphered functional network.

[0218] Non-nerve functional domains 335 may be multiple 340 and typically have one or more defined signatures, e.g., hypervolemia is detectable by reduced electrical impedance of tissue, sympathetic activation via “clammy skin”—reduced galvanic skin resistance and altered ionic composition, apnea via reduced oxygenation measured as reduced skin absorption in the near-infrared end of the electromagnetic spectrum. These signatures can also be characterized by spatial location 345, rate 350 and temporal patterns 355. Locations 345 for breathing include non-contact sensors of breath sounds (e.g. smartphone), movement sensors on the chest or neck to measure breathing, oxygenation on the skin. Signatures 350 for breathing include absence of breath sounds (apnea), loud breath sounds (snoring, arousal), irregular breathing movements (e.g. Cheynes-Stokes breathing). Patterns of these signatures include rapid, slow and other patterns. Numerous other parameters can be measured currently and others may develop in time and be naturally incorporated into this invention by an individual skilled in the art, e.g., tissue concentrations of neurohormones such as B-type natriuretic peptide, cortisol or prolactin from a pharmacological sensor, signal intensity from a photodetector to detect drug concentrations in skin or cutaneous blood vessels, drug or alcohol levels in exhaled breath from an oropharyngeal sensor, drug or alcohol levels in urine from a urethral sensor, cell counts in a tissue sample e.g. sperm counts to test for infertility, and other sensors relevant to the functional domain under consideration.

[0219] Sensed signatures illustrated in FIG. 4 represent the functional domains of that bodily task for an individual person. This forms a type of digital or computerized phenotype for that bodily function. It is recognized that nervous and non-nervous physiological elements can be deeply integrated biologically, but this formulation is a convenient approach to parameterize complex physiology into tracks that can be measured, mathematically modeled and learned. Other more integrated formulations are possible.

[0220] It is important to note that neither all illustrated nor possible signatures are required for the invention to work, i.e. the minimum embodiment. For instance, heart failure can be monitored from the simple measure of weight gain alone. Sleep apnea can be detected from one primary signal—prolonged periods of time without breathing (other signals being supportive). This invention uses the enciphered functional network to weight the most important signature(s) for that individual, either explicitly or implicitly (e.g. via learning), and use whatever signatures are currently available.

[0221] FIG. 5 illustrates modification of the bodily task by effector functions, tailored to sensed signatures for that task. Modifications may comprise therapy, e.g. for sleep-disordered breathing, but may also comprise enhanced normal function, e.g. in sleep quality or alertness. Modification through the enciphered network operates using a feedback loop, in which effector responses are measured by subsequent changes in sensed signatures, to prevent excessive modification. Nerve-related domains 420 can be modified by direct energy delivery 400 to stimulate or suppress a domain. For instance, competitive-stimulation (‘counter’ stimulation) of skin on the abdominal wall (e.g., vibration via a piezoelectric device, heat via an infrared generator) may suppress the sensation of pain in organs innervated by visceral nerves of lumbosacral origin (lower back). Domains 410 may thus lie in the peripheral nerves, such as neck nerves to relieve obstructive sleep apnea or the phrenic nerve to stimulate breathing in central sleep apnea, or central nervous system such as scalp stimulation to modify cranial nerves or light delivery to modulate the ophthalmic nerve or (indirectly) pineal gland activity. In this way, the bodily function can be treated, enhanced or otherwise altered 430. Non-nerve domains 460 can be modified in many ways 440 including vibratory stimulation via a piezoelectric device to stimulate a muscle, infrared heat to reduce muscle spasm to modulate various domains 450 and 460 to modify the bodily function 430. Again, the response to modification from effector functions is individually tailored and monitored by sensed signatures for that bodily task to ensure that excessive and/or deleterious effector functions are not delivered.

[0222] Modulation of nerve-related domains 410 can be linked to modulation of non-nervous domains by modulation connection 415. Moreover, the central and peripheral nervous domains 420 are typically linked to non-nervous system domains 460 by connections 425 which may form other functional domains (e.g. function of adrenocortical glands links the sympathetic nervous system with the endocrine effects of cortisol secretion which impact weight, glucose control, mood, alertness and sleep).

[0223] FIG. 6 indicates several potential body locations 500 for sensors and effectors. Bodily functions can be measured by sensor sites 505 and/or modified by effector sites 510. Sensor sites are shown by open (white) regions, and effector (modifying) sites by filled (black) regions. Their relative physical sizes vary in each individual and are not shown to scale. FIG. 6 indicates sensor locations on the body 500 to detect signatures of the nervous 535, cardiovascular 540, pulmonary 540, gastrointestinal 545, genitourinary 550, skin 550 and other organ systems. Body tasks measured and/or modified by the enciphered functional network include, but are not limited to, sleep and central sleep apnea 515, cognitive performance 520 such as alertness, obstructive sleep apnea 525, and the bodily response to obesity 530. The variety of sensors, sensed signatures, functional domains and bodily tasks are indicated by way of example and not to limit the scope of the invention. These are discussed in more detail with regards to other figures in this disclosure.

[0224] FIG. 6B illustrates a preferred framework for the enciphered functional network. The main elements are 560 arrays of sensors, 561 arrays of effectors, 565 input connections to 570 a processing network. 575 shows output connections from the processing network to health application layers 580 for various bodily or health tasks, including breathing health 581, alertness 582 and cardiac health 583.

[0225] The processing network 570 links the sensor or effector arrays with health states using different implementations of logic. If this is machine learning, then in training the health state feeds backward into the network (hidden layers) to alter weights and associations. For breathing health 581, the sensor array 560 provides sensed signatures (e.g. normal breathing, normal oxygenation, normal heart rate variability) that are linked repeatedly with normal breathing over time for that individual. Sensed signatures from the sensor which deviate from this pattern are now classified as abnormal breathing. The same is true for other body tasks/health states, e.g., alertness, cardiac health.

[0226] The processing network 570 may be rule-based, in which case sensed signatures (sensor states) outside of normal values are flagged as ‘abnormal’. Normal values can be programmed (rules) or learned (hybrid, adaptive-rules). The processing network 570 may also be heuristic-based, database lookup or based upon other associations.

[0227] Processing networks 570 may overlap for various body tasks or functions, as depicted by the overlap in shaded boxes. For instance, a rapid heart rate may be abnormal for breathing health or for cardiac health. On the other hand, the other sensed signatures provide context, because a rapid heart rate may be normal for exercise or alert states.

[0228] FIG. 7 illustrates an example of a body sensor 600, comprising sensor element 605, power source 610, processing components 615, nonvolatile storage 620 (e.g., E2PROM), communication element 625 on a structural platform 630. Several types of sensor elements are illustrated. Sensors include, but are not limited to, photosensitive sensors 640 to detect skin reflectance (indicating oxygenated hemoglobin, perfusion including pulse rates), galvanometers 650 to detect skin impedance or conductance (a measure of body chemistry), transcutaneous or invasive nerve activity (neural electrical activity) or muscle electrical activity (myopotentials), pressure detectors 660 (to detect pressure, e.g., weight, mechanical joint movement or position), thermal detectors 670 to detect temperature (a measure of metabolic activity and other disease states), and chemical detectors 680 to perform assays for norepinephrine or drugs, body pH from the skin, mouth, or elsewhere in the gastro-intestinal or genitourinary tracts, enzymatic profile in the gastrointestinal tract, DNA profile (for instance, a gene chip on the lining of the mouth), and other sensors such as for heart rate, ventilation (breathing).

[0229] The invention can also use external sensors (FIGS. 1, 12-18) that provide a variety of extrinsic or artificial signatures (FIGS. 12-18) to provide cybernetic sensor inputs or effectors to the enciphered functional network.

[0230] FIG. 7A indicates several consumer devices that detect signals and can provide sensed signatures for important functional domains. Consumer devices include, but are not limited to, smartphones 700, smart watches 702, clothing-related sensors, home motion sensors 704, microphones 706, light detectors 708, weighing scales 710, dedicated sound generators such as loudspeakers or headphones 712, thermometer 714 or others. Such devices detect a broad array of sensed signals, if subjected to appropriate processing and transformation by the enciphered functional network.

[0231] In a preferred embodiment, recorded sounds from a smartphone 700 in FIG. 7A are used to detect normal breath sounds, lack of breaths (apnea) and abnormal breath sounds including obstructive sounds and snoring. To accomplish this from a consumer smartphone with no medical devices, the invention and enciphered functional network reduces noise and filters raw sound files, applies physiologically-derived algorithms to detect breaths relative to noise, speech, other physiological sounds. The algorithms also separate sounds from a separate individual (e.g. bed partner), determines their relationship to normal patterns for that individual, and can hence detect disordered breathing. Similar functionality can be achieved with a smartwatch 702, or devices such as a consumer microphone 706. In an alternative embodiment, consumer motion sensors 704 can indicate movement, from which the invention can determine the presence or absence of breaths as above. In a related embodiment, a motion sensor 704 on a bed, chair or other support can detect movement which the invention can identify as breaths. In yet another embodiment, a thermometer 714 can identify fluctuations in temperature near the mouth or nose, which the invention can use to detect breathing and lack of breathing as above. In yet another embodiment, a light source 708 can illuminate the individual's chest at various wavelengths including far red and near infrared light (more penetrating than visible light), and reflected light can indicate chest wall or neck movement which the invention can associate with breathing to determine normal/abnormal breathing. Other embodiments from consumer devices will be apparent to others skilled in the art.

[0232] Other functional domains can be defined by sensed signatures from the array of sensors in FIG. 7A. For instance, diurnal variations in overall or regional body temperature from the thermometer 714 can be used by the invention to monitor sleep, awakeness and general health. Thermal sensors can be in body clothing, on a watch or other near-body location. Near-infrared sensors/cameras can be embedded in walls of a house or other convenient location. Motion sensors 704 can be used to determine when the individual is sleeping versus awake, and active versus sedentary. Sensors can be wearable on shoes/clothes, or fixed in a residence, bed or car, for instance. Weighing scales 710 can provide sensed signals to help in management of weight (obesity) or fluid management (heart failure). For regular assessments, weighing/pressure sensors can be part of smart car seat, smart bed, shoes, in the floor of a room of a house or in other situations. Other functional domains that can be defined by the wide array of available sensors are outlined in the specification, and will be apparent to others skilled in the art.

[0233] In several embodiments, sensed signals from sensors illustrated in FIGS. 7 and 7A will require a personal identification tag to ensure that data is being analyzed from the individual in question, results are communicated to that individual, and/or effector responses are delivered to that individual. This can be accomplished in hardware or software. Hardware embodiments include sensors of biometric information specific to that individual, such as a fingerprint, retinal scan, picture of the iris or unique facial features, composition of sweat, salivary composition (for sensors in the mouth), mucous composition (for sensors in the nostrils or elsewhere in the airway), sensors to analyze heart sounds, breath sounds or speech patterns. Software embodiments include spectral analyses, pattern matching analyses or correlative analyses of these sensed biometric signals compared to known signals from that individual. Known signals from that individual can be sensed at the time of data recording, from a prior stored event, or from a database. In the preferred embodiment of the invention to monitor breathing health, sound files are analyzed after confirming that biometric data matches that from the individual in question, an index of health or disease is made available to that individual and his or her designees, and effector responses are delivered after confirming a match in biometric data to the correct individual.

[0234] Consumer devices in FIG. 7A can also be effector devices for the enciphered functional network. For instance, the smartphone 700 can provide an audible, light-based or vibratory alarm to awake an individual if sleep apnea is detected. These or external devices, e.g. a computer controlled light source, can be activated to advance or retard the sleep/wake cycle tailored for an individual with disturbances of sleep or sleep-related breathing. A smartwatch 702 can provide a vibration signal, auditory alarm or other signal to the individual as an effector response. A loudspeaker 712 can provide stimuli to alter activity, sleep and other functions. A heating or cooling element 714 can alter the propensity of the body to sleep, or alter diurnal cycling. Other applications for the health and disease states in this application will be evident to a person skilled in the art.

[0235] FIG. 7B indicates a preferred embodiment of the invention, which analyzes breathing-related files to monitor and treat the bodily task of breathing. One specific preferred embodiment uses only consumer equipment, records sound files using built-in consumer hardware of a smartphone, uses software on the phone or cloud computing to analyze sound to detect breath signals, generates breath signatures for that individual which can be used to detect and manage breathing disorders. In another preferred embodiment, consumer equipment added to the phone is used to sense signals including but not limited to chest movement, oxygenation, and/or brain activity, to generate other individual signatures. In yet another embodiment, medical grade equipment is used to record signals and generate signatures for the bodily task of breathing. In different sets of embodiments, the invention uses consumer equipment or medical grade equipment to manager other bodily tasks.

[0236] In FIG. 7B, signals are detected in step 720. This includes an individual recognition/ID process, then a calibration step at the start of each detection period. For instance, in one preferred embodiment, the sound intensity of normal breaths is captured, calibrated to distance from the smartphone to the individual, and to sound intensity in that individual at that time. Data is checked and validated in step 722. The first file tag is a check of digital file format 740, such as “.wav” for sound files. Other appropriate file types can be analyzed for breath signals including but not limited to “.mpg” movies of chest wall motion, “.mpg” movies of neck/pharyngeal obstruction, other file types encoding chest wall movement (e.g. files from piezoelectric sensors), commercial home motion sensor files, or file types encoding oxygenation status from skin reflectance or other sensor. File duration is read 742 and files less than a certain duration may be excluded. For analysis of sleep disordered breathing, a typical threshold for adequate duration is >4 hours of recording. File segments that are corrupted are flagged in 744 and file quality metrics are generated in step 746.

[0237] In a preferred embodiment, step 722 checks data for adequacy for breath analysis, such as the presence of periodic activity at the typical rate of one breath every 2-5 seconds (i.e. 0.5 to 0.2 Hz). Another check is whether the periodic activity is likely to be breathing. For sound files, this may include a typical duration of each event of 0.5 to 3 seconds (duration of a breath). For sound files, individual breaths also exhibit typical spectral characteristics, often in the range of 5-15 kHz loudest at 500 Hz-12 kHz, which separates a breath from noise and some aspects of speech. If assessing breathing from chest movement sensor files, the rate should be the same but duration of chest movement will be longer than airflow indicating breath sounds (the chest moves before air begins to flow, and may continue moving after airflow stops). Indexes of movement may be similar for the abdomen, in individuals who use “abdominal breathing” to assist the mechanical function of breathing (ventilation). Notably, indices of breathing movement will differ in periodicity, amplitude, relationship to other sensed signals (e.g. fluctuations in oxygen saturation, variations in ECG amplitude, heart rate) and other properties from non-breathing movement of arms, head or legs, for instance. Metrics can be assessed by spectral decomposition 748, autocorrelation analysis (checking the time shift or amplitude of peaks), or other pattern matching, by individual cutpoints 750, or from a matrix 752 any of which can be stored on database 754 or external medium 756. In the preferred embodiment, the enciphered functional network tailors breath analyses to an individual, and registers ‘normal’ for that individual under conditions such as times of day (longer and slower breaths at night), exertion (shorter and faster breaths), REM sleep (more irregular breath rate and depth compared to Non-REM sleep) and so on.

[0238] Step 724 detects and rejects noise in order to define unreadable epochs. For the preferred embodiment of breath analysis, noise includes sound, chest movement or other signals that do not meet typical criteria for breathing. For instance, a periodic signal at ten times per second (10 Hz) is not human breathing, and is excluded using methods in the art including spectral filtering using Fourier and Inverse Fourier transforms, wavelet analysis and other methods. Some filters are absolute (e.g. the example of breathing rate>5-10 Hz), and some are relative and individualized, e.g. breathing rate in an particular individual may never be >2 Hz during surveillance. After excluding noise, potentially valid signals are passed to the next step e.g. periodic signals at 0.8 Hz that are low amplitude, which could potentially indicate fast shallow breaths (during exertion) or noise. Other signals, e.g. movements of activity, rapid fluctuations in oxygenation or rapid heart rate, could complete the signature of exertion and allow this signal to be analyzed. Conversely, rapid high amplitude signals (from breath sensor or chest movement sensor) without concomitantly high heart rate, oxygenation fluctuations etc are unlikely to be breaths and may be rejected after analysis by the enciphered network. This analysis ends with defining readable epochs in step 726.

[0239] Steps of breath detection 728 and detection of loud breaths 730 are thus tailored to the individual, and calibrated to the sensitivity of the measuring device at that time (step 720, Signal acquisition). Loud breath sounds at night may indicate snores 760, which can occur in normal individuals exacerbated by extreme fatigue or alcohol consumption, as well as individuals with obstructive sleep apnea. Loud breaths can also indicate disturbances 758, i.e. events associated with arousals from sleep or after apnea, coded by the invention as disordered breathing (see definition and glossary of terms).

[0240] All aspects of breath detection 728 and subsequent steps of breath analysis 730-768 are tailored by the enciphered network 729. In this embodiment, the enciphered network incorporates data from other sensors in that individual to help detect each breath, e.g. oxygen waveform fluctuations, fluctuations in ECG amplitude, fluctuations in heart rate.

[0241] Step 732 detection of quiet breaths, apnea and quiet periods is the core of one preferred embodiment for sleep breathing health. Quiet periods, i.e. no sounds recorded, can be determined from step 720 including signal calibration. Separating quiet periods from apnea (i.e. quiet periods between breaths) requires high confidence in the detection of breaths. Identifying quiet breaths requires absolute cutpoints on what constitutes a breath (i.e. a database), and tailored data on what constitutes a breath in that individual under those circumstances (i.e. from the enciphered functional network 729 cross-referenced to other sensed signals). For instance, a quiet sound consistently in phase with chest movement likely relates to quiet breaths, while a quiet sound consistently out of phase/unrelated to chest movement more likely indicates non-breathing sources, which may indicate that the sound detector is too far from the individual to detect breaths. Appropriate steps will be taken, such as informing the individual to move the sound detector closer, or filtering out the sound if it is still unrelated to mechanical ventilation. Intervals between breaths (typically called apnea if >10 seconds in duration) can be related to snores, disturbances and normal breaths.

[0242] Step 734 tailors the algorithmic analysis of the invention to clinical features of that individual. In the preferred embodiment, scoring systems for sleep disordered breathing include the STOP-BANG score, which includes physical examination findings such as neck circumference, and the Epworth sleepiness scale (ESS) indicates symptoms.

[0243] Step 736 tailors the invention to signatures from other functional domains, using the enciphered functional network 729 to combine sensory signatures across functional domains. In the preferred embodiment for breathing health and disorder, several sensory signatures of breathing are combined including airflow (breathing sound files), chest movement (lung expansion), oxygenation (from skin sensors) for that individual (e.g. items 260-290 in FIG. 3). Another preferred embodiment combines signatures of brain function (e.g. nerve signatures from the scalp indicating alertness or sleep, e.g. items 210-260 in FIG. 3, FIG. 4). The enciphered network is able to integrate previously stored patterns of normal and abnormal functional for that individual, and can also integrate databased patterns from other individuals for comparison purposes and/or when data from that individual is sparse.

[0244] Step 738 in FIG. 7B. outputs an index of breathing health. This index can be used to modulate the bodily task by the invention (e.g. FIG. 5,6), to educate the individual, or to assist in clinical evaluation by a traditional (i.e. on-site face-to-face evaluation) health-care provider, online health-care provider networks, or automatic medical treatment device. In a preferred embodiment, the index of breathing health is used for education of the individual, and can be forwarded to a designated health-care provider which can include online web-based health-care provider networks.

[0245] In one preferred embodiment of the invention to monitor breathing health, the index of breathing health is provided only to the individual whose biometric data or login information matches that stored for the individual whose sound files were analyzed. These data can be provided to other designated entities (e.g. a physician's office) if designated by the individual in question. Similarly, effector responses are delivered to the individual, possibly in conjunction with confirming a match in biometric data to the stored information from that individual. This confirmation can be accomplished in hardware or software. Hardware embodiments include sensors of biometric information specific to that individual, such as a fingerprint, retinal scan, picture of the iris or unique facial features, composition of sweat, salivary composition (for sensors in the mouth), mucous composition (for sensors in the nostrils or elsewhere in the airway), sensors to analyze heart sounds, breath sounds or speech patterns. Software embodiments include spectral analyses, pattern matching analyses or correlative analyses of these sensed biometric signals to known signals from that individual. Known signals from that individual can be sensed at the time of data recording, from a prior stored event, or from a database.

[0246] FIG. 7C portrays, for a preferred embodiment of the current invention, analysis of sound files from a consumer smartphone in an individual after informed consent on an institutional review body approved study during prescribed a clinical sleep study. FIG. 7C portrays detected normal breaths, intervals between breaths and snores with no long pauses between breaths (i.e. no apnea). Such sound files may be in several formats including “.wav”. In panel 770 the sound file is checked, validated and noise eliminated (as in FIG. 7B), and represented spectrally after Fourier transform. The resulting graph shows time horizontally for 1 minute (60 seconds), the vertical scale indicates frequencies of sound at each point in time in kHz (from 0 to 20 kHz) and the intensity of color indicates amplitude at each frequency and time.

[0247] In FIG. 7C, panel 770, vertical yellow stripes represent breaths every 2-3 seconds (i.e. rate 0.33 to 0.5 Hz). Panel 771 represents these spectral bands as amplitude-time (peak/trough) sound graphs of spectral amplitude over time scaled in decibels (could be any measure of amplitude). In another embodiment, panel 771 could represent the amplitude of chest wall movement over time, plotted such as excursion at a specific point in millimeters, chest circumference in millimeters, or chest volume in milliliters. Panel 772 presents a clinical sleep study tracing (polysomnogram, PSG) in this individual, obtained simultaneously with the sound files. This PSG includes EEG channels (brain wave activity from scalp electrodes), the EMG (electromyogram), airflow channels, oxygen saturation channels and others.

[0248] Comparing panels 770, 771 and 772, analysis of sound files from the smartphone correlates well with detection of normal breaths and sleep disordered breathing from the simultaneous PSG. Item 773 shows ‘normal breaths’, identified by peak/trough amplitudes in the range of 1.5 to 4.5 dB in this case. Time periods between breaths are evident, but no apnea (>10 seconds without breaths) is seen. Item 774 shows loud sounds with amplitude>4.5 dB classified by the invention as ‘disturbances’ which correlated with disturbances on the PSG. In this case, disturbance on the PSG reflect a cough, but in other instances could indicate a snore, arousal or near arousal after an apneic or hypopneic event, or non-breathing related noises. The absence of apnea or other abnormalities (e.g. reduced oxygenation on PSG) indicates that this case does not represent a sleep breathing disorder. Amplitude ranges and cutpoints are tailored to each individual, to the distance from smartphone to patient and other factors.

[0249] FIG. 7D illustrates another case using a preferred embodiment of the invention, in which sound file analysis from a smartphone alone identified normal breaths, a period of apnea, a period of abnormal disturbance and snoring confirmed in that individual by simultaneous PSG that confirmed sleep disordered breathing. Examining FIG. 7D in detail, panel 780 from 0 to 20 seconds indicates 5 vertical colored bars (i.e. rate of 0.25 Hz), each lasting for <2 seconds when analyzed in panels 781 and 782, of amplitudes 1.5 to 4.5 dB. These bands were classified as normal breaths in this embodiment. Conversely the period from approximately 22 seconds to 45 seconds shows absence of sounds (for >10 seconds) which suggests clinically relevant apnea. Item 785 shows the time period from approximately 45 to 60 seconds showing resumption of loud breaths (amplitude>4.5 dB tailored to this individual), and closely spaced ‘clustered’ sounds of cumulative duration 4-5 seconds between 55 to 60 seconds which were classified by the invention as sound disturbance. Of note, this period corresponds in time to a clinically identified arousal event on blinded analysis of the simultaneous PSG (item 785).

[0250] FIG. 7E shows a flowchart of a preferred embodiment to detect breaths and apneas. The file is read at item 40000, and analyzed spectrally using Fast Fourier transform (item 40010). The spectrogram is analyzed for amplitude over time (item 40020), from which graph peaks and troughs are defined as in FIG. 7C (panel 771) and FIG. 7D (panels 781, 782). A windowed root-mean-square (RMS) envelope function (item 40030) smooths out fluctuations and clarifies peaks (Step 40040). This is seen by comparing panel 781 (pre-windowed RMS) to panel 782 (post-windowed RMS) in FIG. 7D. To avoid identifying low-amplitude noise variations as peaks, preferred embodiments identify peaks if >10% above baseline (item 40050). An index termed ‘prominence’ is used to identify peaks that are used as breaths (item 40060). Prominence is a mathematical function derived from topography, where prominence characterizes the height of a mountain's summit by the vertical distance between it and the lowest contour line encircling it but containing no higher summit within it. In one preferred embodiment, a prominence threshold of >0.21 is used. Such dynamic thresholds can be tailored to the individual based upon one or more of recorded patterns in that individual, recorded patterns in other individuals, patient history, population characteristics, machine learning, disease type, and other patterns. It is to be expected that all thresholds may vary and be dynamically tailored to the individual, with loudness based on proximity of the smartphone to the individual and other factors. After this step, apnea is defined if breaths are absent for a defined period of time (which is >10 seconds in this example). The final list of annotated breaths is then compiled.

[0251] FIG. 7F presents the steps of flowchart in FIG. 7E in a preferred embodiment. Spectral analysis of the sound file in step 41000 produces bands of sound (colored yellow), which are subjected to peak-trough analysis (step 41010), then root-mean-square windowing (step 41020). The baseline value is then computed, and signals higher than 1.1× baseline (i.e. 10% above baseline) are identified (step 41030). This 10% value is empirical, and may be adjusted higher for noisy signals (e.g. higher baseline variations) or when signal-to-noise ratios are lower, or adjusted lower for relatively noise-free signals or when higher sensitivity is needed. The time from about 2 to 22 seconds exhibits loud breaths with several over 4.5 dB in amplitude. These sounds were consistent with loud snoring. There is then a period from 22 to 38 seconds when no breaths are identified, consistent with clinically relevant apnea (item 41070), i.e. no peaks with prominence>0.21 threshold (item 41080), or amplitude>1.5 dB. High amplitude peaks (loud sounds) then resume after about 38 seconds until the end of the tracing. Note that multiple peaks are often tagged very close together in time (item 41090), which are reconciled by selecting the one of higher amplitude. On independent blinded analysis from PSG, this patient had an apneic event with arousal corresponding to the time 22 to 38 seconds, and was diagnosed with clinically relevant obstructive sleep apnea.

[0252] FIG. 7G shows how a preferred embodiment detects loud sounds—which are termed disturbances—and are then further analyzed (via the enciphered functional network) to classify them as loud snores or arousal events on the PSG, or noise. In step 42000 the windowed RMS envelope (e.g. item 782 in FIG. 7D, item 40030 in FIG. 7E, item 41020 in FIG. 7F) is analyzed. The signal is smoothed in step 42010, which can take place by many methods, one of which is high-order median point filter (e.g. 1000 timesteps of 1 ms each). Step 42020 repeats the peak-trough detection step, and step 42030 identifies peaks>10% of baseline (as in item 41030 in FIG. 7F). The 10% threshold can be tailored to the recording and the individual. Step 42040 applies the prominence threshold>0.21, though thresholds are also tailored to the individual and may be dynamic. Step 42050 considers multiple tagged peaks within a close time interval, and identifies the largest peak. Step 42060 finds the area from this tallest peak backward and forward to the baseline, as shown in step 42110 by the shaded area. Larger areas are more likely to be abnormal loud breathing or noise. In a preferred embodiment, areas>1500 analogue-to-digital units (ADU) in dB.Math.milliseconds are identified as disturbance (step 42070, item 42075). Panels 42080 indicates the spectral analysis, 42090 the peak trough graph and 42100 the median filtered peak trough graph, respectively. As shown in FIG. 7D (item 774), and FIG. 7F (item 785), device-detected disturbances correlate with arousals on PSG in a clinical trial.

[0253] FIG. 7H presents more detail on the area calculation to assign a disturbance sound in a preferred embodiment. Item 43000 shows the summary of peak areas for a sound file. Item 43010 indicates an example of the RMS windowed, spectral analysis of a sound file. Each of the peaks shown is analyzed for areas, as indicated by items 43020 and 43030. A threshold area of >1500 Analogue-to-digital units (dB).Math.ms was derived empirically from a clinical trial comparing sound analysis to clinically analyzed PSG files in a derivation cohort of patients, and was then confirmed in a separate validation cohort.

[0254] FIG. 7I illustrates detection of disturbance which corresponds to noise, using the sound analysis from a smartphone in another preferred embodiment. This sound was classified as non-breathing in the simultaneous PSG, and reflected body movement and turning in bed. Item 790 shows a spectrogram of sound with yellow bands that do not plausibly represent breaths, i.e. no yellow bands at 0.2 to 0.5 Hz, bands of duration<2 seconds, and most amplitudes<1.5 dB. Item 791 shows this more clearly. Item 793 highlights the period from approximately 15 to 25 seconds with a broad (>5 seconds) low amplitude (<1.5 B) envelope (panel 791) which correlates with body movement on the PSG (panel 792). Panel 794 shows the period from 37 to 45 seconds shows a broad (5-10 seconds) high amplitude (>4.5 B) envelope which correlates in time with body movement on blinded analysis of the simultaneous PSG (Item 792). Notably, breathing continued throughout this period (see flow channel on PSG, item 792) indicating that the sound file does not indicate breaths. This was a case of the smartphone being too far from the face of the individual to detect breathing, but instead picking up body movement. This time segment of the file was discarded from analysis.

[0255] FIG. 7J shows how a preferred embodiment of the invention analyzes quiet periods (i.e. no sound) versus apnea in between breaths. Item 44000 shows a sound file spectrogram with no clear periodic activity. Item 44010 indicates multiple very closely spaced peaks, each of which has a very low dynamic range. The preferred embodiment filters out these signals because they are not >1.1× baseline, and have a low dynamic range. This file corresponds to a smartphone that is too far from the face of the individual to detect breathing. Item 44020 indicates a similar file, with two potential bands on the spectrogram at approximately 48 and 52 seconds. Item 44030 indicates that these bands meet the criteria outlined above for breaths. The logic of the enciphered functional network will then compare these bands with known breath periods, such as after or before this segment, to determine if these are breaths following a long apneic period, or if these bands are noise in a period when breaths are not captured.

[0256] FIG. 8 is a preferred embodiment of sensed signatures in sleep-breathing disorders. As is typical for many bodily tasks, sleep-disordered breathing impacts multiple nervous and non-nervous system functional domains. Of all of the domains that can be sensed, not all domains need to be sensed in every patient. The actual sensed domains (and hence sensors) used in an embodiment can be tailored to that individual and practical considerations. As seen in FIG. 8, sensor types can include but are not limited to microphones in a smartphone, skin impedance, other electrical sensors (nerve firing in the periphery and on the scalp, and heart rate), temperature, chemical sensors, optical sensors of skin color (that can detect oxygen saturation of peripheral blood), motion sensors and pressure sensors.

[0257] FIG. 9 indicates sample embodiments for effectors of sleep-disordered breathing by the enciphered functional network. These are provided by way of example and in no way limit the scope of effectors or treatment options that the invention can provide for breathing health or other bodily functions. The body 800 is interfaced with effector devices 810, tailored to each modality. For a preferred embodiment of sleep apnea 820 of the central type, effectors may directly stimulate breathing centers including the brain (via low energy scalp stimulation), accessory muscles in the neck and the diaphragm. For central sleep apnea, the invention aims to activate pro-breathing centers, causing the brain to signal higher breathing rates by direct stimulation of scalp regions, or by stimulating sensors of low oxygenation/high carboxyhemoglobin in the finger, by providing CO.sub.2 or equivalent index of low breathing to regions of the periphery that are not harmful. In a preferred embodiment of the invention for obstructive sleep apnea, effectors may directly stimulate pharyngeal and neck muscles to maintain tone and prevent obstruction. Direct stimulation of pro-sleep centers by other methods 850 include stimulation through light exposure of the appropriate wavelength in the visible and infrared spectra. This may stimulate the pineal of other sleep-wake centers in the nervous system. Light can be provided in patterns that are specific to each individual and can be learned by the device. Other pro-sleep sensors include activation of vibratory sensors 860 to mimic the somnorific impact of massage, or stimulation of post-prandial satiety sensors 870 including stimulating peripheral skin sensors of abdominal fullness or hyperglycemia. For both central and obstructive forms of sleep apnea, there is evidence of chest edema (water accumulation) which can be measured as an increased rostral-to-peripheral ratio of skin impedance (FIG. 7). Accordingly, controlled negative pressure in the lower extremities 840 can be used to reverse rostral fluid accumulation. Other specific stimuli can also be provided as familiar to one skilled in the art of sleep disorders, and can be added to the infrastructure of the invention as new modalities and sensed signatures are developed.

[0258] FIG. 10 indicates an example embodiment of sensed signatures for heart failure. As is typical for many bodily tasks, heart failure impacts multiple nervous and non-nervous system functional domains. While the invention may sense any domain, not all domains need to be sensed in every individual, and the actual sensed domains (and hence sensors) can be tailored to a given individual and practical considerations. As seen in FIG. 10, sensor types can include but are not limited to weight sensors (FIG. 7A, item 710) in dedicated scales, in a smart car seat, in shoes, in the floor of a building. Other sensors for heart failure include, skin impedance, electrical sensors to measure nerve firing in the periphery to measure sympathetic tone, and on the scalp to measure EEG, sensors of heart rate, temperature, chemical sensors, optical sensors of skin color (that can detect oxygen saturation of peripheral blood), motion sensors and pressure sensors.

[0259] FIG. 11 indicates an example embodiment of sensed signatures of response to obesity. As typical for many bodily tasks, obesity impacts multiple nervous and non-nervous system functional domains. While the invention can sense any domain, not all domains need to be sensed in every individual, and the actual sensed domain (and hence sensors) can be tailored to a given individual and practical considerations. As seen in FIG. 11, sensor types can include but are not limited to skin impedance, other electrical sensors (nerve firing in the periphery and on the scalp, and heart rate), temperature, chemical sensors, optical sensors of skin color (that can detect oxygen saturation of peripheral blood), motion sensors and pressure sensors.

[0260] FIG. 12 shows an example of sensed signatures for other conditions. One example is for chronic obstructive pulmonary disease which, as is typical for diseases with many complex bodily tasks, impacts multiple nervous and non-nervous system functional domains. While the invention can sense any domain, not all domains need to be sensed in every individual, and the actual sensed domains (and hence sensors) can be tailored to a given individual and practical considerations. As seen in FIG. 12, sensor types can include but are not limited to skin impedance, other electrical sensors (nerve firing in the periphery and on the scalp, and heart rate), temperature, chemical sensors, optical sensors of skin color (that can detect oxygen saturation of peripheral blood), motion sensors and pressure sensors.

[0261] FIG. 13 summarizes the invention, a computerized representation of a complex body task, paired to biological and artificial sensors (cybernetic), and biological and artificial (cybernetic) effectors. The enciphered functional network is trained for specific bodily tasks. In the simplest case, sensed and effector functions are natural physiological functions, such as sensing a painful stimulus from the leg and moving the leg away. In complex embodiments, the invention has the ability to enhance normal function (performance enhancement), enhance impaired function (e.g., sleep-disordered breathing) or treat a disease or in cases where normal function cannot be manifest (e.g., in warfare or other situations of constraint).

[0262] More specifically, FIG. 13 outlines the preferred embodiment of an enciphered network for sleep-disordered breathing. The left panel shows the actual physiology measured for sleep disordered breathing, while the right panel shows the computerized representation of the enciphered functional network.

[0263] In measuring the actual physiology of sleep-disordered breathing in an individual 1200, biological signals are sensed 1205. These include biological signals of control regions 1210 including activation of the amygdala and other parts of the limbic system that control alertness, wakefulness and relate to sleep. These signals have scalp representations that can be detected by skin nerve sensors, but can also be detected by medical devices such as the BOLD signal from functional magnetic resonance imaging, or metabolic images from positron emission tomography in medical applications. Physiologically, sleep is also triggered from intrinsic but natural signals such as darkness, sound (e.g., soothing music or the sound of waves), tactile sense (e.g., massage of parts of the body). The intrinsic sleep control regions of the brain 1210 then integrate these inputs with sensors related to breathing including low oxygenation, measureable in the fingertips 1225, that stimulates breathing, and stimulation of the diaphragm 1220 to enable ventilation of the lungs.

[0264] The schematic shown in the left panel of FIG. 13 is a simplified view of sleep-related-breathing, but it illustrates how a series of sensors and effectors are integrated by the biological control regions. Other sensors and effectors can be involved at other times, and can be measured in connection with the sleep-related breathing. That additional sensed signals can be added and will be adaptively integrated by the enciphered network is a strength of this invention.

[0265] The right panel of FIG. 13 depicts the enciphered network for sleep-disordered breathing in parallel. This also has sensors, control logic and effectors, but these are a combination of biological and engineered (artificial) components. Sensors can detect intrinsic signals 1240 (such as oxygen saturation) or extrinsic signals 1245 (such as the presence, intensity and patterns of visible light). A sensor matrix 1250 then combines these biological and non-biological signals either separately or by multiplexing them, e.g., using a weighted function. The computational logic 1255 is the central processor of the enciphered functional network.

[0266] The computational element 1255 uses symbolic relationships between sensed signals and biological function (e.g., elements 250-290 in FIG. 1). It is linked to a database 1260 to store multiple states for this individual person as training datasets for machine learning (i.e., fuzzy logic, artificial intelligence) in order to learn normal sleep patterns and breathing from disordered ones (elements 250 versus 290 in FIG. 2). This is then mapped to effectors 1265 that can be biological, such as brain regions (related to control regions 1210 and unrelated to control regions 1210) as well as muscles (the diaphragm 1220 as well as other muscles that are less notable but also involved in sleep such as the levator labii superioris alaeque nasi muscles). Effectors can also be cybernetic 1275, in that they interface artificially engineered devices with the body. For instance, a peripheral low oxygen state can be mimicked by small wearable chambers (“treatment gloves”) surrounding one or more fingers that will stimulate breathing from intrinsic sleep-brain control centers (control regions 1210). Similarly, appropriate learned patterns of light or of vibratory stimuli can be applied using appropriate devices, to stimulate sleep-breathing patterns learned from normal states and stored on the database 1260.

[0267] The analysis engine of the enciphered network in FIG. 13 is a symbolic relationship which may be mathematical. This mathematical relationship can be used for mathematical weighting for diagnosis tailoring. Such weighting can be constant and/or adaptive based on learning input streams of sensed signatures. Such weighting can be performed by various methods including but not limited to stochastic methods, correlation methods, calculus based approaches, geometric based methods and spectral methods. The mathematical relationship uses functional relationships between sensed signatures and variations in the body task for that individual—and is not primarily based on theoretical or anticipated relationships. Thus, it may not follow “classical” physiology. For instance, in some patients shoulder pain is associated with heart problems and thus can be part of the sensed signature of heart pain (‘angina’) in such individuals even though shoulder nerves play little or no part in the pathophysiology of heart blood supply. In another example, pain in the leg may elevate nerve activity elsewhere in the body, such that painful leg disorders may be detected using sensors located elsewhere e.g. in more convenient body locations. The functional relationship adapts to sensed signatures and health states tailored to the individual, and such tailoring is based on and may use deterministic (e.g., rule based) or learned methods as outlined throughout this Specification.

[0268] In the simplest case, the symbolic relationship in the enciphered network is a matrix in which a signal X causes a function Y; for instance, a noxious stimulus such as pain sensed by a sensor/sensory nerve in the leg (X) causes activity in a motor nerve causing withdrawal of that leg (Y). This function is not represented in the device based upon a detailed neurophysiological representation of leg sensation (in the primary somatosensory cortex, in the post-central gyrus), or the precise nerves that control the leg. Instead, this function is mapped empirically—sensation on any nerve associated with the painful stimulus can result in actions leading to leg withdrawal.

[0269] The advantage of this approach is that it can analyze the multiple effects of a particular stimulus. For instance, an acute painful stimulus often produces activation on nerves remote from the original site of stimulation. Hence, pain in the leg, that may be inaccessible, may be detected from nerve activity quite distant from the sensation such as the chest wall, that may be more accessible.

[0270] In FIG. 13, generalizing from the example for sleep breathing, sensing is processed and results in output to an effector. For instance, the sensed noxious stimulus can produce an effector function to move the leg, or control a device to administer a pain killing medication or therapy. In other examples that will be discussed below, the stimulus can move a prosthetic limb or alter biological function.

[0271] Moreover, FIG. 13 shows that the enciphered network determines precise action by defining interactions with the device or bodily function. This is a programmed function, depending upon the desired functionality of the invention. This then produces a real output requiring application of energy that results in interaction with the device or a bodily function.

[0272] FIG. 14 illustrates a preferred mode of action of the invention to provide computational enhancement of the bodily function via the enciphered functional network. The flowchart for the invention senses signatures for a given bodily function 1305, comprising biological signals (e.g., breathing rate, finger oxygenation) or extrinsic signals (e.g., tissue impedance indicating volume load, emitted infrared indicating temperature, or carbon dioxide concentrations in exhaled air indicating the efficiency of breathing).

[0273] Item 1310 applies the symbolic model of the enciphered network for an individual, as identified in FIG. 8 to map sensed signals to a bodily function based on practical measurable signatures rather than classical, detailed physiology mapping that may be ill-defined, rapidly changing and inaccessible to measurement.

[0274] As described above, the symbolic model uses machine learning to map sensor input to normal and abnormal function of that bodily functionality. This comprises training sets of different patterns for that specific individual, making the output both personalized and continuously adaptive.

[0275] In FIG. 14, step 1315 transforms an effector (motor) function, i.e., controlled by an existing motor nerve. In step 1320, the motor nerve signal is “re-routed” to control a prosthetic device or another muscle group. For instance, in the case of an amputee, the signature of motor nerve output to the leg may be detected from the skin above the amputation site. The range of sensed nerve activity on the skin may typically be 7-15 Hz (depending on the precise nerve). Sensing these signals, and mapping them to specific movement of a prosthetic limb may enable control of the limb. This control may require subsequent training—for instance, behavioral training in which the individual attempts to flex the amputated limb, and detecting the skin signals as those that will flex the prosthetic limb in that person. Similar personalized mapping is used to train other motions of the prosthesis. In this instance, the invention is one embodiment of a personalized “enciphered nervous system”.

[0276] In FIG. 14, step 1325 is another embodiment—to enhance performance of this body function. Instead of expending the energy required to move a finger, the enciphered network can sense sub-threshold activity of the motor nerve and “boost” the signal to move the finger 1325. This is useful for individuals with nerve degeneration, those with musculoskeletal disorders or those under some form of sedation who would normally not be able to communicate via this finger.

[0277] Furthermore, the invention can 1325 artificially generate signals needed to stimulate the muscle. Since the frequency and amplitude of nerve activity that controls a muscle lies within a range for each individual, the enciphered network can simulate the nerve activity controlling the quadriceps femoris muscle and deliver it programmatically to regions of the skin associated with contraction and relaxation of that muscle for that individual (part of the functional domain). This can be used when the nerve is degenerated or anesthetized (for instance, to prevent pressure ulcers in patients on prolonged ventilation). It can also be used for performance enhancement—for instance, to perform isometric exercises during rest or sleep to prevent or reverse muscle atrophy, or to improve muscle function or increase metabolic rate to lose weight.

[0278] In FIG. 14, step 1330 is another embodiment of the invention—to retask biological motor activity. In this case, it is directed to control an artificial device. This cybernetic application is further developed in FIG. 14. In FIG. 13, instead of actually moving a finger to control a remote control unit for an electronic device, nerve activity below the threshold of actually moving that finger will control the device. This enables functionality without expending as much biological energy, and also in individuals who have lost biological function or are constrained and unable to perform that motor function (e.g., in a military situation). Sensors on the finger detect this subthreshold motor nerve activity (e.g., of lower amplitude than biologically required to move the finger), and the enciphered network converts this to signals that represent play, pause, rewind or other functions and transmits them to control the remote control unit. This may be for a consumer device. Clearly, this function can be extended to training an individual to move a portion of the face to represent the “play” function, and having a sensor transduce this function, and similarly for other surrogate regions of the body and retasked functions.

[0279] In FIG. 14, step 1335 is a distinct embodiment that transforms sensed signals. Step 1340 retasks the sensed signal. For instance, sensation of a specific smell that is trained over time, can elicit a different response or control a device. Step 1345 improves performance, augmenting biological outside of normally sensed ranges. For instance, sensing signals in the “inaudible to humans” frequency range, transducing the signal to the audible range, and transmitting it via vibration (bony conduction) to auditory regions of the brain (auditory cortex) could be used for private communication, encryption, recreation or other purposes. Medically, this invention could be used to treat hearing loss. This same invention with sensors of vibration could be used to compensate for loss of this sensation in diseases such as peripheral neuropathy, by transmitting this sensation to an intact sensation in a nearby or remote part of the body.

[0280] Another embodiment of performance improvement (step 1345) is to increase alertness. Stimulation of the scalp in the temporal region and other function-specific zones can increase brain activity in these regions. The invention tailors stimulation to the enciphered representation of awakeness (i.e., alertness). As a corollary, drowsiness can be detected by the enciphered network and used in a feedback loop to trigger low intensity stimulation by a cutaneous device elsewhere on the body. This has several applications, including detecting and trying to prevent drowsiness while driving, in the intensive care unit during pre-comatose states or during drug-overdoses, as a monitor for excessive alcohol or medication ingestion, or during excessive fatigue states, e.g., in the military.

[0281] Sensors can detect alertness versus drowsiness from large groups of neurons using electroencephalography (EEG) over a wide range of frequencies. EEG signals have a broad spectral content but exhibit specific oscillatory frequencies. The alpha activity band (8-13 Hz) can be detected from the occipital lobe (or from electrodes placed over the occipital region of the scalp) during relaxed wakefulness and increase when the eyes close. The delta band is 1-4 Hz, theta from 4-8 Hz, beta from 13-30 Hz and gamma from 30-70 Hz. Faster EEG frequencies are linked to thought (cognitive processing) and alertness, and EEG signals slow during sleep and during drowsiness states such as coma and intoxication. Alertness vs drowsiness can be potentially detected via other sensors including, but not limited to, visual (e.g. eye tracking or head movement), auditory (e.g. change in speech or breathing sound patterns), and electrical (e.g. ECG measures for autonomic function). The enciphered functional network can integrate these additional sensed data and can assess if they provide useful sensed signatures of normal or abnormal function of that task in that individual.

[0282] In FIG. 14, step 1350 is a function detecting and/or forming a de novo function. One example is creating a cybernetic “sixth sense”—that is, adding to the 5 biological senses using artificial sensors to detect an extended set of stimuli. The set of sensors is nearly infinite, but includes several of particular relevance to the field of industrial or military use, including sensors for alpha or beta-radiation. Once sensed, the enciphered network can transduce this signal to an existing sense, such as vibration delivered through a skin patch to a relatively unused skin region, e.g., lower back. A combat soldier exposed to alpha or beta particles will now “feel” radiation as a programmable/trainable set of vibrations in his lower back. Similarly, sensors for carbon monoxide or other respiratory hazards could be transduced as “sixth senses” into—for instance—low frequency vibration on the nostril. This approach is far more efficient than a visual readout or other existing devices—because they use the enciphered network to essentially reprogram the natural nervous system for these functions.

[0283] FIG. 15 generalizes cybernetic enhancement of body function using the enciphered network. This is a further application beyond the use of intrinsic biological signals. One application is to apply purposeful interventions when natural body functions are constrained, e.g., a soldier can use a finger to activate a device if his/her foot cannot activate a pedal due to an obstacle, or, in an amputee, interfacing a robotic arm to specific nerve fibers that formerly controlled the biological arm.

[0284] FIG. 15 is an embodiment in which intrinsic biological signals and extrinsic non-biological signals are sensed (step 1400). The enciphered network does not simply map learned function to sensed signals, but instead extrapolates from learned functions to create novel function 1410. The enciphered representation of the body function to sensed signals is extended to a personalized network in step 1420 via machine learning. This involves a series of steps, including 1430 multiplexing or otherwise combining intrinsic with extrinsic signals, to programmatically modify external signals in a personalized fashion. Signal multiplexing is performed to achieve the desired function 1440 that may be storage of non biological information (e.g., word processing documents, images) in the patient's brain, i.e., using biological storage as digital memory, and so on. Signals can be combined based on data from this person alone, from a database of multiple individuals (e.g., item 1260 in FIG. 12), or by a technique such as crowd-sourcing in which information from multiple persons is integrated to train the enciphered network. Data from multiple persons could be combined in a formal database, or by applying machine learning to the wider set of sensed signals and biological outputs between individuals (not just for one individual).

[0285] Step 1450 in FIG. 15 shows the effector layer, the interface between the output of the enciphered network for a designed cybernetic function and a series of biological (e.g., motor nerve, muscle) or external (e.g., prosthetic limb, computer) effector devices.

[0286] Several embodiments exist. In step 1460, the invention uses a biological signal to control an external device (e.g., motor nerve control of a prosthetic limb), or an external signal to control a biological function (e.g., external signal stimulation of a skeletal muscle). As described, skeletal muscle is typically stimulated by nerve activity at a frequency of 7-15 Hz (varying with precise nerve distribution, see Dorfman et al. Electroencephalography and Clinical Neurophysiology, 1989; 73: 215-224). Such external stimulation can improve muscle strength by stimulating it, and would enable performance improvement of, e.g., programmable improvement in leg muscle function. Another example is to treat central sleep apnea, using an external sensor of oxygen desaturation (“desat”) to activate a device that stimulates the phrenic nerve and hence the diaphragm. This may have substantial clinical implications.

[0287] FIG. 15 step 1470 shows an embodiment in which the invention replaces a biologically lost or unavailable function in that individual with function from the enciphered network. This is an extension of boosting performance in FIG. 14 (step 1325). For instance, the unavailable function of hearing outside the normal 20 Hz to 20 KHz range can be provided using external sensors and the signal transduced to the audible frequency range (e.g., vibrations delivered via bone conduction using a device placed near the mastoid processes, e.g., attached to the side-arms of eyeglasses, patch attached to head with vibration sensor) or to another sensible modality (e.g., vibration on the arm). In an individual with hearing loss, the sensed signal will lie within the normal but compromised auditory range for this individual.

[0288] In FIG. 15 step 1480, the invention enables biological control of a computer. An example of this function is to provide an intelligent control framework for an infusion pump. For instance, glucose control is not determined simply by the reaction of the pancreas and other sensing regions to plasma glucose. Instead, higher brain centers that control activities of daily living anticipate actions such as imminent exercise or stress, and produce increased heart rate and a hormonal surge (e.g., adrenaline, epinephrine) that in turn increases blood glucose. Current glucose infusion pumps actually cannot mimic such higher cognitive input, and instead wait for drops in glucose from metabolic demands before infusing glucose. Such devices will always lag behind ideal physiological control and will produce suboptimal performance.

[0289] In FIG. 15 step 1490, the invention can provide de novo functionality. This exploits the full potential of the enciphered functional network, in this case for the nervous system, and extends beyond sensory or motor performance improvement in steps 1325 (motor) or 1345 (sensory).

[0290] In FIG. 15 step 1490, novel functionality can be provided for motor function (i.e., previously unavailable movements) or sensory function (i.e., a cybernetic 6.sup.th sense). A large proportion of cerebral processing power is dormant at any given time, but may be activated subconsciously during daily activity (e.g., daydreaming). The enciphered network can access some of this brain capacity to use the biological nervous system as a computer. One task for which the human brain/nervous system is particularly adept is pattern recognition. Recognition of faces, spatial patterns and other complex datasets is performed by people far better than by artificial computers. The selected example trains the individual to detect the pattern via repeated overt or subclinical exposure to an image. The biological response to this image (symbolic representation) is detected by sensors on the temporal or frontal scalp. Again, this is empirical—the primary memory encoding regions do not have to be identified or mapped, and it is sufficient to sense a secondarily activated region of the brain/scalp. Once this is accomplished, detection of the pattern or a similar pattern will subconsciously trigger the response that can be sensed and coded as a “1” or “0” to control a device (e.g., a pattern classifier computer) or cause a certain function—such as to trigger an alarm if this is a dangerous pattern/image.

[0291] FIG. 16 illustrates an embodiment of motor function controlled by the enciphered network. The Flowchart in FIG. 16 provides a preferred embodiment to transform leg movement. A symbolic model is to link motor nerve function, sensed at a signature of the primary motor region (scalp, near the superior portion of the contralateral precentral gyrus) or a secondary region, with a plurality of leg motions in step 1510. Once done, functional mapping can be reprogrammed using external sensed signals (step 1515) including those not normally associated with leg function. An example would be for motion in an index finger to control the leg movement, in patients with leg disease or soldiers who cannot move their leg in a certain task. Functional mapping can also use the existing signal (step 1520).

[0292] In step 1525, a signal multiplexor links the intrinsic or extrinsic signals in order to control the desired programmed function. In step 1530, this is achieved to enhance biological leg function (e.g., via cutaneous/direct electrical stimulation as described). In step 1535, this is performed to control a prosthetic limb.

[0293] FIG. 17 shows an embodiment of enhancing sensory function via the enciphered network. FIG. 17 is an embodiment for enhancing alertness. A symbolic model is created in step 1610 using a signature of sensed scalp nerve activity, e.g., from the temporal region that is empirically associated with alertness. Functional mapping is reprogrammed using intrinsic sensed signatures (step 1615) or signals not normally associated with alertness (e.g., a specific auditory sensed frequency), or the existing scalp signal (step 1620). In step 1625, a multiplexor links the intrinsic and extrinsic signals with an effector to achieve the desired function—electrical stimulation of the scalp to increase alertness (step 1630). Step 1635 provides an alertness monitor to alarm or produce the desired function, and that can detect and try to avoid drowsiness or coma, such as during driving, on the battlefield or from toxin ingestion.

[0294] FIG. 18 depicts an embodiment of the invention to transform sensory function. FIG. 18 is a flowchart of an embodiment to enhance sensory performance—in this case hearing. Step 1710 is the symbolic representation of sensed signals from a readily accessible sensor of the signature near the ear, as well as secondarily associated skin regions. Step 1715 uses sensors to detect signatures of frequencies outside the normally sensed frequency spectrum. Step 1720 uses a signal normally associated with hearing. Step 1725 uses a multiplexor and control logic to transduce the signal to the audible range (step 1730), transmitted via vibration (bony conduction) to the hearing regions of the brain (cochlear nerve/auditory cortex) using a device that could be used for private communication, encryption, recreational or other purposes. Medically, this invention has application as a sophisticated hearing aid. This same invention with vibration sensors compensates for loss of this sensation in diseases such as peripheral neuropathy, by transmitting this sensation to an intact sensation in a different part of the body. At 1735, the multiplexor transduces this signal to a different “surrogate” sensation, e.g., skin stimulation.

[0295] FIG. 19 shows an embodiment to create novel “cybernetic” sensory functions. FIG. 19 is a flowchart of an embodiment to create a cybernetic “sixth sense” (e.g., sensing a biotoxin). The invention summarized in FIG. 19 incorporates information associated with the example of sensing carbon monoxide. Specific sensed signals cause damage, to calibrate sensing and delivery of therapy functions. For instance, exposure to carbon monoxide is dangerous, yet this toxin is often undetected. Federal agencies in the U.S. such as OSHA put a highest limit on long-term workplace exposure levels of 50 ppm, with a “ceiling” of 100 ppm. Exposures of 800 ppm (0.08%) lead to dizziness, nausea, and convulsions within 45 min, with the individual becoming insensible within 2 hours. Clearly, an invention to detect this toxin early and cause biofeedback through the enciphered nervous system may have extremely practical implications in industrial environments. Other nomograms can be developed to identify thresholds for “safe” versus “actionable” exposure to various stimuli including but not limited to chemicals, biological toxins, radiation, electrical stimuli, visual stimuli and auditory stimuli.

[0296] The invention summarized in FIG. 19 can also be used to create novel human functionality, by using the enciphered network to pair sensed biological or external signals to any programmed biological or external device. It thus forms an embodiment of a cybernetic nervous system operating in parallel with the body's natural nervous system. The extent to which these nervous systems are parallel or integrated will depend upon the extent to which sensed signals are multiplexed and effector “control” signals are combined. Examples are discussed below.

[0297] The invention outlined in FIG. 19 thus provides hitherto unavailable programmatic control of plasticity—that is, actually observed at some level on a regular basis in normal life. In the realm of sensory physiology, training can enable an individual to perceive a sensation that was previously present but not registered/recognized. Examples include musical training to detect tonality, or combat training to detect subtle sounds or visual cues. In the realm of motor control, physical training can enable an individual to use muscle groups that were previously unused. In the realm of disease, normal “healing functions” cause healthy regions of the central nervous system to take over functions now lost due to a stroke (cortical plasticity), or unaffected peripheral nerves to take over functions of a nerve lost due to trauma or neuropathy (expansion/plasticity of peripheral dermatomes).

[0298] The current invention extends known interventions based upon cortical plasticity. For instance, it is known that the dermatomal distribution of a functioning peripheral nerve expands when an adjacent distribution is served by a diseased nerve. In other words, the same function can now be served by different regions of the central or peripheral nervous system.

[0299] The invention also substantially extends normal plasticity—by programming desired and directed regions of the body to sense and effect functions normally reserved for other regions of the body that are currently inaccessible (e.g., in military combat) or unavailable (e.g., due to disease).

[0300] The invention also substantially advances normal plasticity by integrating external sensors (e.g., for normally inaudible sound frequencies or sensations) or devices (e.g., prosthetic limbs, other electronic devices) into the ENS.

[0301] FIG. 19 may also include embodiments for enhancing sensory alertness. The steps are analogous to the prior examples. The symbolic model of scalp sensed nerve activity, e.g., in the temporal region is empirically associated with varying alertness levels (self-reported or monitored) in step 1710. This functional mapping is reprogrammed using external sensed signals (step 1715) or signals not normally associated with alertness (e.g., a specific auditory sensed frequency), or the existing scalp signal (step 1720). In step 1725 a signal multiplexor mathematically associates the non-associated or associated signals to program the desired function—electrical stimulation of the scalp to increase alertness (step 1730). Step 1735 provides an alertness monitor that can provide an alarm or actually result in stimulated function (to close the artificial/cybernetic feedback loop in the enciphered nervous system) to detect and try to avoid drowsiness, coma or toxin ingestion.

[0302] FIG. 19 depicts an embodiment to use the ENS to integrate functionality that does not exist in nature into a personalized biofeedback loop—in this case, detecting a toxin. Examples include inhalation of carbon monoxide, a toxic gas that is colorless, odorless, tasteless, and initially non-irritating, that is very difficult for people to detect. Another example is exposure to a biotoxin, that may not be sensed until symptoms and signs of a disease occur hours, days weeks later. The inventive approach to provide a “sixth sense” (step 1800) is cybernetic, since the toxin may produce both a direct signal from a specific sensor (detected at step 1820) and an associated biological signal (step 1830), that are blended (or multiplexed) in the invention. Examples of a direct signal from a dedicated sensor (element 1810) are the chemical detection of carbon monoxide, or a biological assay for an infective agent (viruses, bacteria, fungi). Ideally, this sensor operates in near-real time, although this is not a requirement and if not the case will simply provide a slower, non-real time signal. Examples of an associated biological signal to carbon monoxide—a toxin that is traditionally considered “unsensed”—is the specific cherry red colorimetric change of hemoglobin from carbon monoxide and the non-specific reduction in oxygenated hemoglobin that results when carbon monoxide binds to oxygen binding sites.

[0303] FIG. 19 further depicts that the enciphered nervous system of the invention forms an associative symbolic representation (step 1820) between the direct and associated biological sensed signals. The symbolic relationship may include a direct mathematical transform, such as a quantitative relationship of the sensed signal to carbon monoxide or the associated biological signal of cherry red discoloration of hemoglobin to biologically relevant concentrations. The symbolic relationship may also use an artificial neural network or other pattern-learning or relational approaches to link, e.g., elevated heart rate or oxygen desaturation to the toxin.

[0304] In FIG. 19 step 1840, signals are multiplexed in a non-linear analytical fashion, as defined in the symbolic representation for any specific toxin. Computer logic is then used to control a biological or artificial effector device. Several therapy or monitor functions can be programmed to close a biofeedback loop. For instance, the signal from the normally unsensed toxin can be transduced into a specific signal on a naturally sensed “channel” (step 1860), e.g., low intensity vibration on skin on the nostril (intuitively linked with inhalation), or stimulation of skin over a scalp region normally associated with deoxygenation. This latter biofeedback uses information from training related to the individual person (contributing to the personalized enciphered nervous system), or a database of symbolic representations from many individuals associating related stimuli (here, de-oxygenation) to biological signals. This is an example of a population-based, or potentially crowd-sourced enciphered nervous system. Another biofeedback option is therapeutic (1860)— delivery of an antidote, by sending control signals to a device. For carbon monoxide exposure, therapy includes increasing oxygen concentrations (using hyperbaric oxygen in extreme cases) and administering methylene blue.

[0305] Nomograms of the detrimental impact of sensed signals are used to calibrate sensing and delivery of therapy functions from the enciphered nervous system. For carbon monoxide, exposures at 100 ppm (0.01%) or greater can be dangerous to human health. Accordingly, in the United States, Federal agencies such as OSHA put a highest limit on long-term workplace exposure levels of 50 ppm, but individuals should not be exposed to an upper limit (“ceiling”) of 100 ppm. Exposures of 800 ppm (0.08%) lead to dizziness, nausea, and convulsions within 45 min, with the individual becoming insensible within 2 hours. Clearly, detecting this toxin early would have extremely practical implications in industrial environments, for instance. Other nomograms can be developed to identify thresholds for “safe” versus “actionable” exposure to various stimuli including but not limited to chemicals, biological toxins, radiation, electrical stimuli, visual stimuli and auditory stimuli.

[0306] FIG. 20 provides another embodiment using the enciphered network to access to the processing power of the natural nervous system to perform an arbitrary task, such as pattern recognition (step 1905). This embodiment of the invention is based upon 3 concepts. First, that the brain is more efficient at some tasks than even the most powerful and well-programmed artificial electronic computers. Pattern recognition, e.g., facial recognition, is an excellent example that is easily accomplished by most people yet that is suboptimal by computers even with very sophisticated programming. Second, that the brain output from a presented stimulation can be sensed. Third, that the brain has unused capacity that can be accessed for this purpose. For instance, for neural processing, only a minority is used even in highly stressful human activities such as warrior combat (e.g., 40% capacity used). In highly focused, non-life-or-death situations, a minority is still used, likely 20-40%, e.g., NBA finals, SAT testing. Therefore, there is substantial residual capacity at any one time. This third item also presents safety limits, however, and in the case of pattern recognition, the invention must not be used for bioencoding images or data that would be emotionally harmful or sensitive.

[0307] Steps 1910 and 1915 link the pattern (e.g., a face) to the biological sensed response—for instance, activity of nerves in the scalp over the parietal lobes of the brain, or over the forehead indicating “recognition”. This is used to create the elements of enciphered nervous system for this task (step 1920). This will be personalized, but can also take inputs from a multi-person (population, crowd-sourced) encyphered nervous system. Once this link has been made, then presentation of the pattern will result in a “sensed” biological pattern, which is used by the multiplexer or control logic in step 1925 to deliver a “1” (recognized) or “0” (not recognized) to control a device (step 1930) (e.g., external computer classifier) or stimulate the individual via a surrogate sensation (step 1935) (e.g., vibration at the left upper arm if a recognized pattern is detected). Uses for this invention include pure biocomputing (pattern recognition of familiar or abstract shapes/codes), formally encoding and enhancing memory of faces for a particular person, and security such that only a hostile pattern/face elicits a specific surrogate sensation or activates a device. One other advantage of this approach over waiting for a cognitive recognition of the pattern is that this can function as a “background process” and/or provide faster pattern recognition.

[0308] Thus, this invention can improve and enhance function of traditional senses, if a device is used that integrates sensors that sense outside the normal physiological range can be used to enhance the range of normal physiological sensation. For instance, sensing signals in the “inaudible to humans” part of the frequency spectrum, transducing the signal to the audible range, and transmitting it via bony conduction using a device could be used for private communication, encryption, recreational or other purposes. Medically, this invention could be used to compensate for hearing loss. This same invention with sensors of vibration could be used to compensate for loss of this sensation in certain neurological diseases such as peripheral neuropathy, by transmitting this sensation to an intact sensation in a different part of the body.

[0309] FIG. 21 is a block diagram of an illustrative embodiment of a general computer system 2000. The computer system 2000 can be the signal processing device 114 and the computing device 116 of FIG. 1. The computer system 2000 can include a set of instructions that can be executed to cause the computer system 2000 to perform any one or more of the methods or computer based functions disclosed herein. The computer system 2000, or any portion thereof, may operate as a standalone device or may be connected, e.g., using a network or other connection, to other computer systems or peripheral devices. For example, the computer system 2000 may be operatively connected to signal processing device 114, analysis database 118, and control device 120.

[0310] In operation as described in FIGS. 1-21, the modification or enhancement of the nervous system of the body by creating and using an enciphered functional network as described herein can be used to enhance performance in normal individuals or restore or treat lost function in patients.

[0311] The computer system 2000 may be implemented as or incorporated into various devices, such as a personal computer (PC), a tablet PC, a personal digital assistant (PDA), a mobile device, a palmtop computer, a laptop computer, a desktop computer, a communications device, a control system, a web appliance, or any other machine capable of executing a set of instructions (sequentially or otherwise) that specify actions to be taken by that machine. Further, while a single computer system 2000 is illustrated, the term “system” shall also be taken to include any collection of systems or sub-systems that individually or jointly execute a set, or multiple sets, of instructions to perform one or more computer functions.

[0312] As illustrated in FIG. 21, the computer system 2000 may include a processor 2002, e.g., a central processing unit (CPU), a graphics-processing unit (GPU), or both. Moreover, the computer system 2000 may include a main memory 2004 and a static memory 2006 that can communicate with each other via a bus 2026. As shown, the computer system 2000 may further include a video display unit 2010, such as a liquid crystal display (LCD), a light emitting diode such as an organic light emitting diode (OLED), a flat panel display, a solid state display, or a cathode ray tube (CRT). Additionally, the computer system 2000 may include an input device 2012, such as a keyboard, and a cursor control device 2014, such as a mouse. The computer system 2000 can also include a disk drive unit 2016, a signal generation device 2022, such as a speaker or remote control, and a network interface device 2008.

[0313] In a particular embodiment, as depicted in FIG. 21, the disk drive unit 2016 may include a computer-readable medium 2018 in which one or more sets of instructions 2020, e.g., software, can be embedded. Further, the instructions 2020 may embody one or more of the methods or logic as described herein. In a particular embodiment, the instructions 2020 may reside completely, or at least partially, within the main memory 2004, the static memory 2006, and/or within the processor 2002 during execution by the computer system 2000. The main memory 2004 and the processor 2002 also may include computer-readable media.

[0314] In an alternative embodiment, dedicated hardware implementations, such as application specific integrated circuits, programmable logic arrays and other hardware devices, can be constructed to implement one or more of the methods described herein. Applications that may include the apparatus and systems of various embodiments can broadly include a variety of electronic and computer systems. One or more embodiments described herein may implement functions using two or more specific interconnected hardware modules or devices with related control and data signals that can be communicated between and through the modules, or as portions of an application-specific integrated circuit. Accordingly, the present system encompasses software, firmware, and hardware implementations.

[0315] In accordance with various embodiments, the methods described herein may be implemented by software programs tangibly embodied in a processor-readable medium and may be executed by a processor. Further, in an exemplary, non-limited embodiment, implementations can include distributed processing, component/object distributed processing, and parallel processing. Alternatively, virtual computer system processing can be constructed to implement one or more of the methods or functionality as described herein.

[0316] It is also contemplated that a computer-readable medium includes instructions or receives and executes instructions 2020 responsive to a propagated signal, so that a device connected to a network 2024 can communicate voice, video or data over the network 2024. Further, the instructions 2020 may be transmitted or received over the network 2024 via the network interface device 2008.

[0317] While the computer-readable medium is shown to be a single medium, the term “computer-readable medium” includes a single medium or multiple media, such as a centralized or distributed database, and/or associated caches and servers that store one or more sets of instructions. The term “computer-readable medium” shall also include any medium that is capable of storing, encoding or carrying a set of instructions for execution by a processor or that cause a computer system to perform any one or more of the methods or operations disclosed herein,

[0318] In a particular non-limiting, example embodiment, the computer-readable medium can include a solid-state memory, such as a memory card or other package, which houses one or more non-volatile read-only memories. Further, the computer-readable medium can be a random access memory or other volatile re-writable memory. Additionally, the computer-readable medium can include a magneto-optical or optical medium, such as a disk or tapes or other storage device to capture carrier wave signals, such as a signal communicated over a transmission medium. A digital file attachment to an e-mail or other self-contained information archive or set of archives may be considered a distribution medium that is equivalent to a tangible storage medium. Accordingly, any one or more of a computer-readable medium or a distribution medium and other equivalents and successor media, in which data or instructions may be stored, are included herein.

[0319] In accordance with various embodiments, the methods described herein may be implemented as one or more software programs running on a computer processor. Dedicated hardware implementations including, but not limited to, application specific integrated circuits, programmable logic arrays, and other hardware devices can likewise be constructed to implement the methods described herein. Furthermore, alternative software implementations including, but not limited to, distributed processing or component/object distributed processing, parallel processing, or virtual machine processing can also be constructed to implement the methods described herein.

[0320] It should also be noted that software that implements the disclosed methods may optionally be stored on a tangible storage medium, such as: a magnetic medium, such as a disk or tape; a magneto-optical or optical medium, such as a disk; or a solid state medium, such as a memory card or other package that houses one or more read-only (non-volatile) memories, random access memories, or other re-writable (volatile) memories. The software may also utilize a signal containing computer instructions. A digital file attachment to e-mail or other self-contained information archive or set of archives is considered a distribution medium equivalent to a tangible storage medium. Accordingly, a tangible storage medium or distribution medium as listed herein, and other equivalents and successor media, in which the software implementations herein may be stored, are included herein.

[0321] Thus, a system and method of diagnosis tailoring for an individual, and capable of controlling effectors to deliver therapy or enhance performance, have been described. Although specific example embodiments have been described, it will be evident that various modifications and changes may be made to these embodiments without departing from the broader scope of the invention. Accordingly, the specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings that form a part hereof, show by way of illustration, and not of limitation, specific embodiments in which the subject matter may be practiced. The embodiments illustrated are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived, such that structural and logical substitutions and changes may be made without departing from the scope of this disclosure. This Detailed Description, therefore, is not to be taken in a limiting sense, and the scope of various embodiments is defined only by the appended claims, along with the full range of equivalents to which such claims are entitled.

[0322] Such embodiments of the inventive subject matter may be referred to herein, individually and/or collectively, by the term “invention” merely for convenience and without intending to voluntarily limit the scope of this application to any single invention or inventive concept if more than one is in fact disclosed. Thus, although specific embodiments have been illustrated and described herein, it should be appreciated that any arrangement calculated to achieve the same purpose may be substituted for the specific embodiments shown. This disclosure is intended to cover any and all adaptations or variations of various embodiments. Combinations of any of the above-described embodiments, and other embodiments not specifically described herein, may be used and are fully contemplated herein.

[0323] The Abstract is provided to comply with 37 C.F.R. § 1.72(b) and will allow the reader to quickly ascertain the nature and gist of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims.

[0324] In the foregoing description of the embodiments, various features are grouped together in a single embodiment for the purpose of streamlining the disclosure. This method of disclosure is not to be interpreted as reflecting that the claimed embodiments have more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive subject matter lies in less than all features of a single disclosed embodiment. Thus the following claims are hereby incorporated into the Description of the Embodiments, with each claim standing on its own as a separate example embodiment.