Abstract
The present invention relates generally and specifically to combining biological sensors with external machines using machine learning to form computerized representations that can control effectors to deliver therapy or enhance performance.
Claims
1. A method for enhancing mental alertness in an individual, the method comprising: detecting signals of normal and abnormal function of mental alertness of an individual at one or more sensors, wherein the sensors measure signals of breathing; mathematically processing said signals using an enciphered functional network to create a symbolic representation of the mental alertness of the individual; and delivering effector responses based on the symbolic representation in order to enhance or reinstate the mental alertness.
2. The method of claim 1, wherein the detection is performed without requiring knowledge of the precise physiological mapping of the signals or without requiring use of signals empirically associated with alertness.
3. The method of claim 1, wherein the sensed signals include one or more signals from blood vessel flow, vasomotor activity, skin electrical activity, heart rate or heart rate variations, breathing rate and cellular edema.
4. The method of claim 1, wherein the effector response is tailored to the individual based on the symbolic representation of mental alertness.
5. The method of claim 1, wherein the effector response includes biofeedback to enhance mental alertness.
6. The method of claim 1, wherein the effector response includes delivering energy attenuated to the individual's symbolic representation of mental alertness.
7. The method of claim 6, wherein energy modulates the mental alertness to a normal or enhanced state based on the symbolic representation created by the enciphered functional network.
8. The method of claim 7, wherein the energy includes stimulation to the scalp.
9. The method of claim 6, wherein the effector response includes audio stimulation.
10. The method of claim 1, wherein the mental alertness enhancement is demonstrated by measurable improvement in a task.
11. A method of detecting mental alertness and/or fatigue in an individual comprising: detecting signals of normal and abnormal function of mental alertness of an individual at one or more sensors, wherein the sensors measure signals of breathing; mathematically processing said signals using an enciphered functional network to create a symbolic representation of the mental alertness and/or mental fatigue of the individual; and based on the symbolic representation, delivering an effector response sufficient to modify the individuals mental alertness and/or mental fatigue.
12. The method of claim 11, wherein the detection is performed without requiring knowledge of the precise physiological mapping of the signals or without requiring use of signals empirically associated with alertness.
13. The method of claim 11, wherein the sensed signals include one or more signals from blood vessel flow, vasomotor activity, skin electrical activity, heart rate or heart rate variations, breathing rate and cellular edema.
14. The method of claim 11, wherein the effector response includes biofeedback to enhance mental alertness.
15. The method of claim 14, wherein the effector response includes delivering energy attenuated to the individual's symbolic representation of mental alertness.
16. The method of claim 15, wherein energy modulates the mental alertness to a normal or enhanced state based on the symbolic representation created by the enciphered functional network.
17. The method of claim 11 The method of claim 1, wherein the mental alertness enhancement is demonstrated by measurable improvement in a task.
18. A method for affecting the performance of a task by enhancing mental alertness of an individual, the method comprising: detecting signals of normal and abnormal function of said mental alertness of an individual at one or more sensors, wherein the sensors measure signals of breathing, and wherein said detection is performed without requiring knowledge of the precise physiological mapping of the signals or without requiring use of signals empirically associated with alertness; mathematically processing said signals to create a symbolic representation of the mental alertness of the individual; and delivering effector responses based on the symbolic representation in order to enhance or reinstate the mental alertness, wherein said enhanced mental alertness is measured by performance of the task.
19. The method of claim 18, wherein the sensed signals further include one or more of blood vessel flow, vasomotor reactivity, skin electrical conductivity, heart rate or heart rate variations, breathing rate, and cellular edema.
20. The method of claim 18, wherein the effector response comprises biofeedback to enhance mental alertness.
21. The method of claim 20 wherein the biofeedback in in the form of electrical energy to stimulate said individual.
22. The method of claim 18, wherein the effector response comprises audio stimulation.
23. The method of claim 18, wherein the method includes an enciphered network that can be trained through machine learning.
24. The method of claim 18, wherein the effector response comprises energy emanating from a device external to the body.
25. A method for detecting mental fatigue in an individual as measured by performance of a task, the method comprising: detecting abnormal and normal breathing signals from the body using sensors, said detection not requiring the precise physiological mapping of the signals or detection of signals empirically associated with fatigue; mathematically processing said signals to create a symbolic representation of the fatigue of the individual; and delivering effector responses based on the symbolic representation in order to reduce fatigue, wherein said reduced fatigue is measured by enhanced performance of the task.
26. The method of claim 25, wherein the sensed signals further include one or more of blood vessel flow, vasomotor reactivity, skin electrical conductivity, heart rate or heart rate variations, breathing rate, and cellular edema.
27. The method of claim 25, wherein the effector response comprises biofeedback to enhance mental alertness.
28. The method of claim 27, wherein the biofeedback in in the form of electrical energy to stimulate said individual.
29. The method of claim 25, wherein the effector response comprises audio stimulation.
30. The method of claim 25, wherein the method includes an enciphered network that can be trained through machine learning.
31. The method of claim 25, wherein the effector response comprises energy emanating from a device external to the body.
Description
BRIEF DESCRIPTION OF THE DRAWINGS
[0148] Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, in which:
[0149] Some embodiments are illustrated by way of example and not limitation in the figures of the accompanying drawings, that show:
[0150] FIG. 1. Schematic representation of the invention, including sensors for biological and non-biological signals, a processing unit that produces a symbolic representation of specific nervous functions, or an encyphered nervous system, and a control unit to alter nervous function or control a device.
[0151] FIG. 2. Flowchart indicating general functionality of the invention for biological and non-biological sensed signals.
[0152] FIG. 3. Flowchart indicating functionality of the invention for biological sensed signals.
[0153] FIG. 4. Flowchart indicating functionality of the invention for non-biological (external) sensed signals.
[0154] FIG. 5. Flowchart indicating one embodiment for enhancing 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).
[0155] FIG. 6. Flowchart for indicating one 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).
[0156] FIG. 7. Flowchart indicating another 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.
[0157] FIG. 8. Flowchart indicating another embodiment for providing a sensory function that does not currently exist. This is illustrated for integrating sensation from a biosensor for a biotoxin.
[0158] FIG. 9. Flowchart indicating another embodiment for using the invention and enciphered nervous system to provide de novo functionality. Illustrated is using unused computational capacity of the nervous system to perform pattern recognition in lieu of programming an artificial computer for this purpose.
[0159] FIG. 10. Block diagram of an illustrative embodiment of a general computer system.
[0160] FIG. 11 shows a schematic representation of the invention, including biological sensors or external sensors, a signal processing unit and a computing device that uses machine learning and can interface a database to create a symbolic 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.
[0161] FIG. 12 illustrates the relative relationship of sensors, sensed signatures for functional domain(s), the enciphered functional network (with analysis engine), the effector group(s) for a functional domain for a bodily function.
[0162] FIG. 13 shows a flowchart illustrating how the enciphered functional network represents a bodily function in an individual person, as functional domains represented by sensed signatures. Sensed signatures are analyzed by machine learning algorithms relative to desired and undesired behavior, and to databases in the enciphered network of population behavior or historical behavior of that individual, to monitor function, guide and assess response to therapy.
[0163] FIG. 14 shows an example of sensed signatures for a given bodily function, for functional domains representing physiology in the nervous system and not in the nervous system. The portfolio of sensed signatures becomes the measured representation of that bodily function for an individual person.
[0164] FIG. 15 shows examples of modifying bodily function using the enciphered network. 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.
[0165] FIG. 16 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.
[0166] FIG. 17 shows examples of a body sensor, with 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. 11, 22-28) that provide a variety of extrinsic or artificial signatures (FIGS. 22-28).
[0167] FIG. 18 shows an example of an embodiment of sensed signatures in sleep disordered breathing.
[0168] FIG. 19 shows an example of an embodiment of effector locations for sleep disordered breathing.
[0169] FIG. 20 shows an example of an embodiment of sensed signature for heart failure.
[0170] FIG. 21 shows an example of an embodiment of sensed signature of body response to obesity.
[0171] FIG. 22 shows an example of an embodiment of sensed signatures for other conditions.
[0172] FIG. 23 shows an enciphered (symbolic) network model for physiology of sleep-disordered breathing.
[0173] FIG. 24 shows enhancement of body function using enciphered network.
[0174] FIG. 25 shows cybernetic enhancement of body function using enciphered functional network.
[0175] FIG. 26 shows an example of a transformation of motor function. The flowchart shows one embodiment for enhancing 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).
[0176] FIG. 27 shows an example of enhancing 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).
[0177] FIG. 28 shows an example of transformation of 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.
[0178] FIG. 29 shows an example of creating a novel cybernetic sensory function. The flowchart indicates an embodiment for providing a sensory function that does not currently exist. This is illustrated for integrating sensation from a biosensor for a biotoxin.
[0179] FIG. 30 shows another example of creating a novel cybernetic sensory function. The flowchart indicates an embodiment for using the biological nervous system for recognition of a desired pattern.
[0180] FIG. 31 shows computer hardware for machine learning.
DETAILED DESCRIPTION
[0181] FIG. 1 illustrates an example system to modify and enhance functionality of the nervous system in a human being. Specifically, the example system 100 is configured to access external signals from biological sensors 104 and from external sensors 110.
[0182] Biological sensors 104 include, but are not limited to, sensors for transcutaneous or invasive nerve activity (neural electrical activity), muscle electrical activity (myopotentials), of mechanical activity (mechanoreceptors), skin resistance (a measure of body chemistry), body temperature (a measure of metabolic activity and other disease states), body pH (from the skin, mouth, or other regions of the gastro-intestinal or genitourinary tracts), enzymatic profile (for instance, from a probe in the gastrointestinal tract), DNA profile (for instance, a gene chip on the lining of the mouth), heart rate, ventilating (breathing) rate, or any other body signal.
[0183] External sensors 110 can sense biological signals, from that individual, from another individual or from a database of signals 118.
[0184] External sensors 110 can provide many types of information including, but not limited to, those normally sensed including pressure/physical movement (tactile, touch sensation), temperature (thermal sensation), sound (auditory sensation), electromagnetic radiation in the visible spectrum (visual sensation), movement (a measure of muscle function and balance).
[0185] External sensors 110 can provide information related to normal sensation but that is not normally sensed including, but not limited to, the invisible electromagnetic spectrum (such as gamma radiation, X-rays, radiowaves), 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).
[0186] External sensors 110 can provide information that is not normally sensed including, but not limited to, toxins such as carbon monoxide or excessive carbon dioxide, forms of radiation (such as alpha and beta radiation), biotoxins such as toxins of Escherichia coli bacteria associated with food poisoning (type 0157), anthrax or other agents. Clearly, such information would be of value for military and security applications.
[0187] In FIG. 1, signals (internal or external) are delivered either wirelessly or via wired communication to a signal processing device 114 functioning in concert with a computing device 116 that has access to an analysis database 118. The computing and signal processing devices communicate with a control device 120, that in turn directly controls a biological device (e.g., a body part of body function) 108 or an external device 112 (e.g., remote control, medical device). The computing, signal processing and control devices with sensors and effector devices together form an enciphered nervous system (ENS).
[0188] FIG. 2 give more detail on the inventive process of the enciphered nervous system. Signals are sensed (205) as either non-biological (210) or biological (215), although these signals can be combined and multiplexed.
[0189] In FIG. 2, element 220 is the core computational element that functions as the central nervous system of the ENS. This computational element forms a symbolic relationship between the signal and a biological function. This symbolic relationship is mathematical. Notably, this relationship is empirical and functionalit is not based on a detailed neurophysiological mapping of the function. It is also not necessarily based on the primary classical neurological loop. For instance, sufficient pain in the leg causes elevated nerve activity in other parts of the body. This can be sensed as associated with the pain sensation, from a sensor in a more convenient part of the body. The empirical functional relationship is mathematical, and can be deterministic (e.g. equation based), or can be trained/learned such as via neuronal network.
[0190] In the simplest case, the symbolic relationship 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 empiricallysensation on any nerve associated with the painful stimulus can result in actions leading to leg withdrawal.
[0191] An inventive advantage of this approach is that it exploits the pleiotropic effects of any 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.
[0192] In FIG. 2, Step 220 is followed by a mechanical action (225) or biological action (230). For instance, the sensed noxious stimulus can produce an effector function to move the leg (biological, 230), or control a device to administer a pain killing medication or therapy (device, 225). In other examples that will be discussed below, the stimulus can move a prosthetic limb (mechanical, 225) or alter biological function (230).
[0193] FIG. 2 further indicates that the precise action is determined by the ENS by defining the interaction with the device or bodily function (step 235). This is a programmed function, depending upon the desired functionality of the invention. This then produces a real output requiring energy application (step 240) that results in interaction with the device (245) or a bodily function (250).
[0194] Thus, FIG. 2 summarizes the invention as a parallel artificial central and peripheral nervous system that pairs sensed function to effector function in programmed ways. In the simplest case, the sensed and effector functions are natural physiological functions, such as sensing a painful stimulus from the leg and moving the leg away. In more complex and practical ways, the invention provides the ability to enhance normal function (performance enhancement), enhance impaired function to treat a disease or in cases where normal function cannot be manifest (e.g. in warfare or other situations of constraint).
[0195] The invention as described in FIG. 1 and FIG. 2 may require general information on how certain 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. The present invention detects this toxin early and causes biofeedback through the enciphered nervous system, hence having extremely practical implications in industrial environments. Other nomograms to identify thresholds for safe versus and actionable exposure to various stimuli are contemplated, including but not limited to chemicals, biological toxins, radiation, electrical stimuli, visual stimuli and auditory stimuli. The present invention provides methods and systems to detect and provide feedback mechanisms and to develop such practical applications for therapy or creating safer environments.
[0196] The invention as described in FIG. 1 and FIG. 2 can also be used to create totally novel human functionality, by using the engineered artificial encyphered nervous system to pair sensed biological or external signals to any programmed biological or external device function. It thus forms an embodiment of a cybernetic nervous system operating in parallel with the body's natural nervous system. The extent to which this 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.
[0197] The invention as described in FIG. 1 and FIG. 2 thus provides hitherto unavailable programmatic control of plasticitythat 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 undiseased 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).
[0198] The invention also substantially extends normal plasticityby 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).
[0199] 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.
[0200] 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.
[0201] Important safety issues must be raised at this stage. While no untoward, dangerous or otherwise undesired functionality has been observed with this invention, certain limits must be imposed. First, stimulation intensity provided by the device can be controlled such that painful or dangerous levels are not reached. Second, sensory input can be controlled such that disturbing or undesired levels are not reached. Third, any sensor or device (effector) used desirably may have acceptable and tested safety profiles.
[0202] FIG. 3. Provides a flowchart for the ENS to sense biological signals (305) and complete an effector function based upon this information. The first step is the symbolic model, as described above (310). This involves mapping a sensed signal to a functionbut not in the classical, detailed fashion typical of neurophysiology. This is a practical mapping step, for which mapping of secondary regions associated with a function is sufficient if that secondary region is readily accessible.
[0203] In FIG. 3, step 315 is to transform a function, controlled by an existing motor nerve (sensed biological signal). In step 320, the sensed 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 input 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 said limb. This control may require subsequent trainingfor 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. Thus, this invention represents one embodiment of a personalized enciphered nervous system.
[0204] In FIG. 3, step 325 is a distinct functionto improve motor performance. It is well known that electrical stimulation of nerves that control a muscle can stimulate that muscle. The frequency and amplitude of this nerve activity lies within a range, but may be specific for an individual. Thus, this ENS function is to sense motor nerve activity controlling the quadriceps femoris muscle, for instance. The frequency and amplitude of nerve activity in regions of the skin associated with contraction and relaxation of that muscle are stored for an individual (part of the symbolic representation). An external device is then used to reiterate this functionality in a programmatic way. This can be used to stimulate the muscle during rest, to perform isometric exercises that will improve muscle function. This may also increase metabolic rate and cause weight loss.
[0205] In FIG. 3, step 330 is another distinct functionto retask biological motor activity to control a device. For instance, instead of actually moving a finger to control a remote control unit for an electronic device, the user may attempt to move that finger without expending sufficient energy to move the finger. Sensors on the finger are tuned detect this motor stimulation (that may be low amplitude), and the symbolic representation in the ENS converts this to signals representing play, pause, rewind or other functions and transmits them to control said consumer remote control unit. 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.
[0206] In FIG. 3, step 335 is a transformation of sensed signals. This is another functionality of the invention. Step 340 involves enhancing the sensed signal. An example is performance improvement (step 345), involving augmenting biological senses using sensors that detect outside of normally sensed ranges. For instance, sensing signals using a sensor of inaudible to humans part of the frequency spectrum, transducing the signal to the audible range, and transmitting it via vibration (bony conduction) to the hearing regions of the brain (auditory cortex) 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.
[0207] Another example of performance improvement (step 345) 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 such stimulation to the symbolic representation of awakeness (i.e. alertness). As a corollary, drowsiness can be detected via the ENS and used as part of a feedback loop to trigger low intensity stimulation elsewhere on the body where a cutaneous device can be placed. 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, and as a monitor for excessive alcohol or medication ingestion.
[0208] Sensors can detect alertness versus drowsiness from large groups of neurons such as using electroencephalography (EEG) that produce a wide range of frequencies. EEG signals, for instance, 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, in this invention 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.
[0209] In FIG. 3, step 350 uses the invention for de novo sensory function. One example is creating a digital or cybernetic sixth sensethat is, adding to the existing 5 senses using external 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 ENS transduces 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 intofor instancelow frequency vibration on the nostril. This approach is far more efficient than a visual readout or other existing devicesbecause they use the ENS to essentially reprogram the natural nervous system for these functions.
[0210] FIG. 4 illustrates a flowchart of an embodiment in which non-biological signals are sensed (step 405) and processed by the encyphered nervous system. The symbolic representation between the body function and sensed signals is now extended to a non-personalized ENS (step 410), in order to incorporate external signals of a generic form albeit potentially tailored to the individual person. This ENS can be derived from a database of multiple individuals, or by a technique such as crowd-sourcing in which information from multiple persons connected by a social network is used to provide functionality.
[0211] Step 415 in FIG. 4 involves designing a programmable body function to be associated with the sensed external (non-biological) signal. This function can include motor control of a device such as prosthetic limb in step 420. Another example would be more far reachingto use an external trigger signal to improve function in an existing natural muscle group (item 430). As described, skeletal muscle is typically stimulated by nerve activity at a frequency of 7-15 Hz (that varies with the precise nerve distribution, see Dorfman et al. Electroencephalography and Clinical Neurophysiology, 1989; 73: 215-224). Providing this stimulation can improve muscle strength by stimulating it, and would enable performance improvement of e.g. leg muscles from a programmable signal. A medical example would be to treat central sleep apnea, by having an external sensor of oxygen desaturation to activate a device that stimulates the phrenic nerve and hence the diaphragm. This has substantial clinical implications.
[0212] In FIG. 4 step 425, the invention uses an external signal to improve performance in a sensory function. In an example already used, hearing can be enhanced by using external sensors of auditory signals outside the normal frequency range to be transduced to the normal frequency range as vibrations delivered via bone conduction to the cochlear nerve in the inner ear using a device placed near the mastoid processes (e.g. attached to the side-arms of eyeglasses).
[0213] In FIG. 4 step 440 the invention exploits the full potential of the enciphered nervous system to create novel programmable functionality by pairing an external sensed signal with intrinsic nervous function. The example of performance improvement (sensory or motor) in step 445 has been discussed.
[0214] In step 450, FIG. 4, the invention can provide de novo functionality. 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 ENS can programmatically access this capacity to use the intrinsic 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 most artificial computers. The selected example trains the individual to detect said pattern via repeated 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 mappingand it is sufficient to represent a secondarily activated region of the brain/scalp. Once this is accomplished, then detection of the pattern or a similar pattern will subconsciously trigger said 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 functionsuch as to trigger an alarm if this is a dangerous pattern/image.
[0215] The Flowchart in FIG. 5 provides a preferred embodiment to transform leg movement. A symbolic model of motor nerve function, sensed near the primary motor region (scalp, near the superior portion of the contralateral precentral gyrus) or at a secondary region, is associated with a plurality of leg motions in step 510. Once done, this functional mapping can be reprogrammed using external sensed signals (step 515) or signals not normally associated with leg function (e.g. moving an index finger in patients with leg disease or soldiers who cannot move their leg in a certain task), or the existing signal (step 520). In step 525 a signal multiplexor is able to mathematically associate the non-associated or associated signals in order to control the desired programmed function. In step 530 this is enhancement of the biological leg function (e.g. via cutaneous/direct electrical stimulation as described). In step 535 this is via control of a prosthetic limb.
[0216] FIG. 6 provides an embodiment 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 610. This functional mapping is reprogrammed using external sensed signals (step 615) or signals not normally associated with alertness (e.g. a specific auditory sensed frequency), or the existing scalp signal (step 620). In step 625 a signal multiplexor mathematically associates the non-associated or associated signals to program the desired functionelectrical stimulation of the scalp to increase alertness (step 630). Step 635 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.
[0217] FIG. 7 is a flowchart of an embodiment to enhance performance in a sensory functionin this case hearing. Step 710 forms the symbolic representation using sensed signals from a readily accessible sensor (not just ear, but potentially secondarily associated skin regions). Step 715 uses sensors that detect outside of normally sensed ranges in inaudible to humans parts of the frequency spectrum. Step 720 uses a signal normally associated with hearing. Step 725 uses a multiplexor and control logic to transduce the signal to the audible range (step 730), transmitted via vibration (bony conduction) to the hearing regions of the brain (cochlear nerve/auditory cortex) 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. Step 735 transduces this signal to a different surrogate sensation.
[0218] FIG. 8 depicts an embodiment to use the ENS to integrate functionality that does not exist in nature into a personalized biofeedback loopin 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 or weeks later. The inventive approach to provide a sixth sense (step 805) is cybernetic, since the toxin may produce both a direct signal from a specific sensor (detected at step 810) and an associated biological signal (step 815), that are blended (or multiplexed) in the invention. Examples of a direct signal from a dedicated sensor (element 810) 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 monoxidea toxin that is traditionally considered unsensedis 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.
[0219] FIG. 8 further depicts that the enciphered nervous system of the invention forms an associative symbolic representation (step 820) 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.
[0220] In FIG. 8 step 825, 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 830), 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 encyphered nervous system. Another biofeedback option is therapeutic (step 835)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.
[0221] 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 and actionable exposure to various stimuli including but not limited to chemicals, biological toxins, radiation, electrical stimuli, visual stimuli and auditory stimuli.
[0222] FIG. 9 provides a flowchart for an embodiment in which the enciphered nervous system enables access to the processing power of the natural nervous system to perform an arbitrary task, in this case pattern recognition (step 905). 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. of faces, 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. This third item presents safety limits, 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.
[0223] Steps 910 and 915 mathematically link the pattern (e.g. a face) to the biological sensed responsefor 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 920). 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, that is used in step 925 to deliver a 1 (recognized) or 0 (not recognized) to control a device (step 930) (e.g. external computer classifier) or stimulate the individual via a surrogate sensation (step 935) (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.
[0224] FIG. 10 is a block diagram of an illustrative embodiment of a general computer system 1400. The computer system 1400 can be the signal processing device 114 and the computing device 116 of FIG. 1. The computer system 1400 can include a set of instructions that can be executed to cause the computer system 1400 to perform any one or more of the methods or computer based functions disclosed herein. The computer system 1400, 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 1400 may be operatively connected to signal processing device 114 and analysis database 118.
[0225] In operation as described in FIGS. 1-9, the modification or enhancement of the nervous system of the body by creating and using an enciphered nervous system (ENS) as described herein can be used to enhance performance in normal individuals or restore or treat lost function in patients.
[0226] The computer system 1400 may also 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 1400 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.
[0227] As illustrated in FIG. 10, the computer system 1400 may include a processor 1402, e.g., a central processing unit (CPU), a graphics-processing unit (GPU), or both. Moreover, the computer system 1400 may include a main memory 1404 and a static memory 1406 that can communicate with each other via a bus 1426. As shown, the computer system 1400 may further include a video display unit 1410, such as a liquid crystal display (LCD), an organic light emitting diode (OLED), a flat panel display, a solid state display, or a cathode ray tube (CRT). Additionally, the computer system 1400 may include an input device 1412, such as a keyboard, and a cursor control device 1414, such as a mouse. The computer system 1400 can also include a disk drive unit 1416, a signal generation device 1422, such as a speaker or remote control, and a network interface device 1408.
[0228] The invention may include, as depicted in FIG. 10, the disk drive unit 1416 may include a computer-readable medium 1418 in which one or more sets of instructions 1420, e.g., software, can be embedded. Further, the instructions 1420 may embody one or more of the methods or logic as described herein. In a particular embodiment, the instructions 1420 may reside completely, or at least partially, within the main memory 1404, the static memory 1406, and/or within the processor 1402 during execution by the computer system 1400. The main memory 1404 and the processor 1402 also may include computer-readable media.
[0229] The invention may also include, 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 inventive system encompasses software, firmware, and hardware implementations.
[0230] In accordance with the invention, 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.
[0231] It is also contemplated that a computer-readable medium includes instructions 1420 or receives and executes instructions 1420 responsive to a propagated signal, so that a device connected to a network 1424 can communicate voice, video or data over the network 1424. Further, the instructions 1420 may be transmitted or received over the network 1424 via the network interface device 1408 (FIG. 10).
[0232] 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.
[0233] 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.
[0234] In accordance with the inventive 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.
[0235] 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.
[0236] As stated above, a system and method for enhancing and modifying complex functions of the nervous system and other parts 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.
[0237] The invention modulates and enhances complex and higher bodily functions by modulating a series of functional domains. Typically, the complex function will include a component of brain or nervous activity. One innovation is the creation of an enciphered (symbolic) representation to model the complex function. Such a representation model may also be called a network, and is learned in this invention. This is created by, then used to interpret sensed signals from functional domains that comprise the function. The enciphered network is then used to effect change. In one preferred embodiment, this is applied to improve sleep apnea, but other embodiments modulate heart failure, obesity, alertness, mood, memory and mental performance or cognition.
[0238] FIG. 11 illustrates an example system to modify and enhance complex body functions in a human being. Specifically, the example system 1000 is configured to access external signals from biological sensors 1040 and from external sensors 1100.
[0239] The biological sensors 1040 can sense biological signals, from an individual, from another individual, or from a database of signals 1180. The biological sensors 1040 can be wearable.
[0240] External sensors 1100 can sense biological signals, from an individual, from another individual or from a database of signals 1180. In turn, signals may arise from the central nervous system, peripheral nervous system, cardiovascular system, pulmonary system, gastrointestinal system, genitourinary system, skin or other systems.
[0241] External sensors 1100 can provide many types of information including, but not limited to, those normally sensed including pressure/physical movement (tactile, touch sensation), temperature (thermal information, infrared sensing), chemical (galvanic skin resistance, impedance, detection of specific ions from the skin), sound (auditory sensation), electromagnetic radiation in the visible spectrum (visual sensation), movement (a measure of muscle function and balance).
[0242] External sensors 1100 can also provide information related to normal sensation but that is not normally sensed including, but not limited to, the invisible electromagnetic spectrum (such as gamma radiation, X-rays, radiowaves), 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).
[0243] External sensors 1100 can provide information outside normal sensory modalities including, but not limited to, toxins such as carbon monoxide or excessive carbon dioxide, forms of radiation (such as alpha and beta radiation), biotoxins such as toxins of Escherichia coli bacteria associated with food poisoning (type 0157), anthrax or other agents. Clearly, such information would be of value for military and security applications.
[0244] In FIG. 11, signals are delivered either wirelessly or via connected communication to a signal processing device 1140 functioning with a computing device 1160 that has access to an analysis database 1180. The computing device 1160 and signal processing device 1140 communicate with a control device 1200, which in turn controls a biological device 1080 or an external device 1120. The biological device 1080 is an effector device, which can be wearable by the individual. The computing, signal processing and control devices with sensors and effectors together form an enciphered functional network (EFN).
[0245] FIG. 12 illustrates the relationship between sensors, sensed signatures for functional domain(s), the enciphered functional network (with analysis engine) and the effector group(s) within the functional domain for a bodily function. At item 1500 one can see the entire functional network domain for a particular bodily function, such as sleep or breathing. At 1550 are illustrated sensors 1, 2, . . . n that are used to provide sensed signatures 1600 for this functional domain. The enciphered functional network 1650 for this functional domain controls and analyzes the information from the sensors and sensed signatures Of note, the enciphered network can recruit additional sensors or stored patterns (such as from a database, shown in FIG. 13) depending on its learned or programmed behaviors. Many forms of analysis can be performed as discussed below. Item 1700 shows that the enciphered functional network includes communication with an effector group for that bodily function, which in turn signals effectors 1, 2, . . . n at step 1750. A key element of the invention is interconnectivity and links between each element within/with the enciphered functional network, indicated by double arrows.
[0246] FIG. 13 gives more detail on the enciphered functional network for a normal bodily function or abnormal bodily functions. The list of bodily functions addressed by this invention are broad, and typically span multiple physiological systems (represented as functional domains). They may include but are not limited to sleep, sleep disordered breathing, cognition, mental performance, response to obesity, response to heart failure.
[0247] In FIG. 13, a body function is represented by nervous system 2200 and non-nervous system (non-neural) 2600 networks. The networks 2200, 2600 comprise respective functional domains 2300, 2700, defined by signatures 2400, 2800 based on a variety of sensors. This produces nerve and non-nerve signatures for the body function, which can be normal 2500 and abnormal 2900or desired 2500 and undesired 2900. It should be noted that the networks can interact via interactions 2250 and signatures may be inter-related by relationships 2450.
[0248] Machine learning algorithms of the enciphered functional network are enabled using artificial intelligence (autobot, fuzzy logic circuits). This can be done via neural networks (e.g., 3 layer back-propagation networks or other designs), techniques of deep learning, heuristics, linear classifiers or other forms of fuzzy logic. An important feature of such systems is that they do not need to know much about the specifics of human pathophysiology, but need to learn information about factors influencing behavior that is provided by the sensed signatures. They are thus well suited to the problem of complex bodily functions that are often incompletely defined or mapped pathophysiologically. This is not structured by theoretical textbook classification schemes. Certain elements of the system can be layered as rule-based, using for instance deterministic solutions from a database such as the dermatomal distribution of a nerve in the shoulder or the fact that some fluctuations in skin oxygenation reflect heart rate.
[0249] Thus, the symbolic model of simple and complex functions is akin to representing something that is visualized by an impressionist painter rather than a detailed physiological representationby one trained in the realist school. Again, this approach is based largely on the premise that in addition to the primary physiological systems required for a task, that is difficult to precisely define, secondary networked regions become involved.
[0250] Machine learning nominally links signatures with normal function 2500 in order to create a patient specific range to detect abnormal function 2900 as outliers. In practice, the best results are obtained when the machine learning algorithms perform repeated pattern classification interactions 2550 between sensed signatures for normal 2500 and abnormal 2900 functions. This interconnectivity is necessary, but its complexity makes the system ideally suited for a computational machine learning paradigm to modify and treat the networks 2350.
[0251] In FIG. 13, digital learned representations enable personalized diagnosis and therapy. A database of learned networks (representations) between individuals forms the core of a multimodal digital network of population health and disease, that is actionablei.e., can be used to monitor and treat disease or improve performance. For health care or screening purposes, this database (component 2150) is 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.
[0252] FIG. 14 provides detail of signatures sensed 3100 by the invention to represent a given bodily function in an individual person. Body functions comprise multiple functional domains, broadly classed as primarily nervous system related and not nervous system related physiology. Sensed nerve signatures 3150 would typically represent the sensing location 3200, patterns of activity 3250 (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 3300 (e.g., the fundamental or dominant frequency of a spectrum or first peak on an autocorrelation function).
[0253] 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.
[0254] Non-nerve signatures 3350 represent other modalities 3400 that are not primarily in the nervous system. Represented modalities 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 measurable as reduced skin absorption in the near-infrared end of the electromagnetic spectrum. These signatures also possess information on location 3450, rate 3500 and temporal patterns over time 3550. Numerous other parameters are currently possible and may develop over time and be incorporated into this invention, e.g., tissue concentrations of neurohormones such as B-type natriuretic peptide 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 penile sensor, and other sensors relevant to the functional domain under consideration.
[0255] The network of sensed signatures exemplified in FIG. 14 becomes the measured representation of that bodily function for an individual person. This is a form of digital phenotype of components of the bodily function. It is recognized that nervous and non-nervous physiological elements are 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.
[0256] Note that not all possible measured signatures are needed for the invention to workin simple clinical practice, heart failure can be monitored quite well from the simple measure of weight gain alone; this invention uses machine learning to mathematically weight the most important signature but also to use information from whatever is currently available.
[0257] FIG. 15 illustrates modification of the bodily function using the enciphered network, tailored to sensed signatures. Modifications include therapy, such as for sleep-disordered breathing, but can also include enhancement of normal function for that individual. Modification through the enciphered network operates via a feedback loop, in which response is measured to prevent excessive or deleterious modification. Nerve-related domains can be modified by direct energy delivery 4000 to stimulate or suppress a domain. For instance, counterstimulation of skin on the abdominal wall (e.g., by vibration via a piezoelectric device, heat via an infrared generator) may suppress the sensation of pain from organs supplied by visceral nerves of lumbosacral origin (lower back). Domains 4100 may thus lie in the peripheral nervous or in the central nervous system 4200, 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 4300. Non-nerve domains can be modified 4400 in many ways including vibratory stimulation via a piezoelectric device to stimulate a muscle, infrared heat to reduce muscle spasm to modulate the domain 4500 and network 4600 to modify the bodily function 4300. Notably, modification is individually tailored via personalized sensory signatures and the enciphered network.
[0258] Modulation of nerve-related domains 4100 can be linked to modulation of non-nervous domains by modulation connection 4150. Moreover, the central and peripheral nervous network 4200 can be linked to the non-nervous system physiologic network by network connection 4250.
[0259] FIG. 16 indicates several potential body 5000 locations for sensing signatures and modifying different functional domains. Bodily functions can be measured by sensor 5050 and/or modified by effector 5100 sites. Sensor locations are shown by open (white) regions, and effector (modifying) locations by filled (black) regions. Their relative size varies in each individual and is not shown to scale. FIG. 16 indicates sensor locations on the body 5000 to detect signatures of the nervous 5350, cardiovascular 5400, pulmonary 5400, gastrointestinal 5450, genitourinary 5500, skin 5500 and other domains. Body functions measured and/or modified by the enciphered functional network include sleep and central sleep apnea 5150, cognitive performance 5200 such as alertness, obstructive sleep apnea 5250, and the bodily response to obesity 5300. A variety of signatures 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.
[0260] FIG. 17 illustrates an example of a body sensor 6000, comprising sensor element 6050, power source 6100, processing components 6150, nonvolatile storage 6200 (e.g., E2PROM, powered RAM), communication element 6250 on a structural platform 6300. Several types of sensor elements are illustrated. Biological sensors include, but are not limited to, photosensitive sensors 6400 to detect skin reflectance (indicating oxygenated hemoglobin, and perfusion), galvanometers 6500 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 6600 (to detect pressure, e.g., weight, mechanical joint movement or position), thermal detectors 6700 to detect temperature (a measure of metabolic activity and other disease states), and chemical detectors 6800 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).
[0261] The invention can also use external sensors (FIGS. 11, 22-28) that provide a variety of extrinsic or artificial signatures (FIGS. 22-28) to provide cybernetic sensor inputs or effectors to the enciphered functional network.
[0262] FIG. 18 indicates an example embodiment of sensed signatures in sleep-breathing disorders. As typical for many bodily functions, sleep-disordered breathing impacts multiple nervous and non-nervous system domains. While all domains can be sensed, not all domains need to be sensed in every patient, and the actual sensed domains (and hence sensors) can be tailored to signatures in a given individual and practical considerations. As seen in FIG. 18, 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.
[0263] FIG. 19 indicates example embodiments for various effector or treatment options for sleep-disordered breathing using the enciphered functional network. These are provided by way of example and in no way limit the scope of effectors and treatment options that can be provided for this condition or other bodily functions. The body 8000 is interfaced with effector devices 8100, tailored to each modality. For sleep apnea 8200 of the central type, examples include direct stimulation of breathing centers including the brain (via low energy scalp stimulation), accessory muscles in the neck and the diaphragm. For obstructive sleep apnea, examples include direct stimulation of pharyngeal and neck muscles to maintain tone and prevent obstruction. For central sleep apnea, the invention can activate pro-breathing centers, tricking the brain to breathe more by stimulating sensors of low oxygenation/high carboxyhemoglobin in the finger, by providing CO2 or equivalent index of low breathing to regions of the periphery that are not harmful. For central and obstructive forms of sleep apnea, there is evidence that chest edema accumulates and can be measured as increased rostral-to-peripheral ratio of skin impedance (FIG. 13). Accordingly, controlled negative pressure in the lower extremities 8400 can reverse this rostral fluid accumulation. Direct stimulation of pro-sleep centers by other methods 8500 include stimulating the pineal gland through light exposure of the appropriate wavelength in the visible and infrared spectra. Light can be provided in patterns that are specific to each individual. Other pro-sleep sensors include activation of vibratory sensors 8600 to mimic the somnorific impact of massage, or stimulation of post-prandial satiety sensors 8700 including stimulating peripheral skin sensors of hyperglycemia. 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.
[0264] FIG. 20 indicates an example embodiment of sensed signatures in response to heart failure. As typical for many bodily functions, heart failure impacts multiple nervous and non-nervous system domains. While all domains can be sensed, not all domains need to be sensed in every patient, and the actual sensed domains (and hence sensors) can be tailored to signatures in a given individual and practical considerations. As seen in FIG. 20, 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.
[0265] FIG. 21 indicates an example embodiment of sensed signatures in response to obesity. As typical for many bodily functions, obesity impacts multiple nervous and non-nervous system domains. While all domains can be sensed, not all domains need to be sensed in every patient, and the actual sensed domains (and hence sensors) can be tailored to signatures in a given individual and practical considerations. As seen in FIG. 21, 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.
[0266] FIG. 22 shows an example of sensed signatures for other conditions. One example is for chronic obstructive pulmonary disease which, as typical for many bodily functions, impacts multiple nervous and non-nervous system domains. While all domains can be sensed, not all domains need to be sensed in every individual. The actual sensed domains (and hence sensors) can be tailored to signatures in a given individual and practical considerations. As seen in FIG. 22, 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.
[0267] FIG. 23 summarizes the invention, a computerized representation of a complex body function, paired to biological and artificial (cybernetic) sensors, and biological and artificial (cybernetic) effectors. The enciphered functional network is trained by machine learning algorithms for specific bodily functions. 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 more 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).
[0268] More specifically, FIG. 23 outlines 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.
[0269] 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.
[0270] The schematic shown in the left panel of FIG. 23 is of course 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. This is a strength of the invention, that additional sensed signals can be added and will be adaptively integrated by the enciphered network.
[0271] In the right panel of FIG. 23, the parallel enciphered network for sleep-disordered breathing 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.
[0272] The computational element 1255 forms a symbolic relationship between sensed signals and biological function (e.g., elements 1250-1275 in FIG. 23). 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.
[0273] The symbolic relationship of the enciphered network in FIG. 23 is a mathematical relationship. This relationship is empirical and functional that uses machine learned relationships between sensed signatures and body function in each individualand not on detailed neurophysiological mapping. It is thus distinct and may not be concordant with classical neurophysiology. For instance, sufficient pain in the leg causes elevated nerve activity in other parts of the body. This will produce associated with leg pain signals in sensors located more conveniently in the body. The empirical functional relationship is mathematical, and can be deterministic (e.g., equation based), or can be trained/learned such as via neural network.
[0274] In the simplest case, the enciphered symbolic relationship 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 empiricallysensation on any nerve associated with the painful stimulus can result in actions leading to leg withdrawal.
[0275] 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.
[0276] In FIG. 23, 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.
[0277] Moreover, FIG. 23 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.
[0278] FIG. 24 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).
[0279] Item 1310 applies the symbolic model of the enciphered network, as identified in FIG. 18 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.
[0280] 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 individual, that are both personalized and continuously adaptive.
[0281] In FIG. 24, 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 trainingfor 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.
[0282] In FIG. 24, step 1310 is another embodimentto 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 1314. 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.
[0283] Furthermore, the invention can artificially generate signals needed to stimulate the muscle (FIG. 24, 1312). 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 enhancementfor 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.
[0284] In FIG. 24, 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. 24. In FIG. 25, 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.
[0285] In FIG. 24, 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.
[0286] 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.
[0287] 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.
[0288] In FIG. 24, step 1335 is a function detecting and/or forming a de novo function. One example is creating a cybernetic sixth sensethat 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 intofor instancelow frequency vibration on the nostril. This approach is far more efficient than a visual readout or other existing devicesbecause they use the enciphered network to essentially reprogram the natural nervous system for these functions.
[0289] FIG. 25 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.
[0290] FIG. 25 is an embodiment in which intrinsic biological signals and extrinsic non-biological signals are sensed (step 1360). The enciphered network does not simply map learned function to sensed signals, but instead extrapolates from learned functions to create novel function 1365. The enciphered representation of the body function to sensed signals is extended to a personalized network in step 1365 via machine learning. This involves a series of steps, including 1370 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 1375 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. 23), 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).
[0291] Step 1380 in FIG. 25 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.
[0292] Several embodiments exist. In step 1385, 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.
[0293] FIG. 25 step 1390 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. 24 (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 to the inner ear using a device placed near the mastoid processes, e.g., attached to the side-arms of eyeglasses) 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.
[0294] In FIG. 25 step 1392, 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.
[0295] In FIG. 25 step 1393, 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).
[0296] In FIG. 25 step 1393, 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 empiricalthe 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 functionsuch as to trigger an alarm if this is a dangerous pattern/image.
[0297] FIG. 26 illustrates an embodiment of motor function controlled by the enciphered network. The Flowchart in FIG. 26 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).
[0298] 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.
[0299] FIG. 27 shows an embodiment of enhancing sensory function via the enciphered network. FIG. 27 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 functionelectrical 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.
[0300] FIG. 28 depicts an embodiment of the invention to transform sensory function. FIG. 28 is a flowchart of an embodiment to enhance sensory performancein 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.
[0301] FIG. 29 shows an embodiment to create novel cybernetic sensory functions. FIG. 29 is a flowchart of an embodiment to create a cybernetic sixth sense (e.g., sensing a biotoxin). The invention summarized in FIG. 29 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.
[0302] The invention summarized in FIG. 29 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.
[0303] The invention outlined in FIG. 29 thus provides hitherto unavailable programmatic control of plasticitythat 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 undiseased 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).
[0304] 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.
[0305] The invention also substantially extends normal plasticityby 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).
[0306] 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.
[0307] FIG. 29 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 functionelectrical 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.
[0308] FIG. 29 depicts an embodiment to use the ENS to integrate functionality that does not exist in nature into a personalized biofeedback loopin 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 monoxidea toxin that is traditionally considered unsensedis 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.
[0309] FIG. 29 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.
[0310] In FIG. 29 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.
[0311] 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.
[0312] FIG. 30 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.
[0313] Steps 1910 and 1915 link the pattern (e.g., a face) to the biological sensed responsefor 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.
[0314] 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.
[0315] Important safety issues must be raised at this stage. While no untoward, dangerous or otherwise undesired functionality has been observed with this invention, certain limits must be imposed. First, no stimulation intensity provided by the device can reach painful or dangerous levels. Second, no sensory input can be allowed to reach disturbing or undesired levels. Third, any sensor or device (effector) should have acceptable and tested safety profiles.
[0316] FIG. 31 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. 10. 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.
[0317] In operation as described in FIGS. 11-30, 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.
[0318] 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.
[0319] As illustrated in FIG. 31, 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.
[0320] In a particular embodiment, as depicted in FIG. 31, 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.
[0321] 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.
[0322] 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.
[0323] 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.
[0324] 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.
[0325] 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.
[0326] 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.
[0327] 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.
[0328] Thus, a system and method of identifying a source of a heart rhythm disorder, by identification of rotational of focal activation in relation to one or more spatial elements associated with the source of the heart rhythm disorder, 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.
[0329] 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.
[0330] 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.
[0331] 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.
