A neural analysis and treatment system includes a computing device with a memory for storing an application that is executable on a processor to receive amplitude-integrated electroencephalography (aEEG) and range-EEG (rEEG) measurements associated with a patient. The systems determine a spectral edge frequency (SEF) measurement from the received EEG measurements, and determine one or more neural characteristics of the patient according to the determined SEF, aEEG, and rEEG measurements. These neural characteristics may then be used to identify and implement an appropriate therapeutic treatment.

An injection sensing device (ISD) (e.g., wearable patch) is paired with an external device (e.g., a medication delivery pen and/or smart phone, iPad, computer) via wireless link or wireline connection. The ISD senses fluctuations in local skin temperature during an injection and provides to the external device captured data from the sensor relating to medicine delivery to a patient to ensure complete delivery and minimize MDD misuse or malfunction or inaccuracies in dosing. The ISD or external device can use captured data and corresponding time stamps to determine flow informatics such as flow rate, total dose delivered, and dose completion status. An LED on the ISD indicates delivery in progress and/or delivery completion.

An object of the present invention is to provide an atrial fibrillation detection system in which: the extent of irregularity in measured pulse intervals is calculated; even in cases where an extrasystole has occurred, detection of a false positive is reduced even when the extrasystole is not excluded; and highly reliable assessment results are obtained. An atrial fibrillation detection system comprising: pulse interval measurement means 4 that measures the pulse intervals of a heart; pulse interval conversion means 8 that performs conversion, using a prescribed function, so that the extent of variation in the pulse intervals R obtained by the pulse interval measurement means 4 is substantially fixed; entropy computation means 9 that calculates entropy S from pulse interval images r obtained through conversion by the pulse interval conversion means 8; and comparative assessment means 10 that compares the entropy S calculated by the entropy computation means 9 and a threshold value, and that, in cases where the entropy S is greater than the threshold value, assesses that atrial fibrillation is occurring.

A diagnostic Electrochemical Impedance Spectroscopy (EIS) procedure is applied to measure values of impedance-related parameters for one or more sensing electrodes. The parameters may include real impedance, imaginary impedance, impedance magnitude, and/or phase angle. The measured values of the impedance-related parameters are then used in performing sensor diagnostics, calculating a highly-reliable fused sensor glucose value based on signals from a plurality of redundant sensing electrodes, calibrating sensors, detecting interferents within close proximity of one or more sensing electrodes, and testing surface area characteristics of electroplated electrodes. Advantageously, impedance-related parameters can be defined that are substantially glucose-independent over specific ranges of frequencies. An Application Specific Integrated Circuit (ASIC) enables implementation of the EIS-based diagnostics, fusion algorithms, and other processes based on measurement of EIS-based parameters.

A method and a device for detecting OSAHS provided that the method comprises: acquiring a vibration signal of a subject during sleep, and determining a breathing signal of the subject (S1), wherein the breathing signal comprises an inspiration signal generated upon inspiration and an expiration signal generated upon expiration; acquiring strength of a first vibration signal within a specified frequency range and superimposed on the inspiration signal, and strength of a second vibration signal within a specified frequency range and superimposed on the expiration signal adjacent to the inspiration signal (S2); and comparing, according to a preset method, the strength of the first vibration signal with the strength of the second vibration signal, and determining, according to a comparison result, whether the subject is snoring (S3). Since the detection is performed synchronously with breathing, the invention can prevent interference caused by coughing, speaking and other acoustic signals transmitted in the air, thereby significantly increasing accuracy in determining OSAHS. Moreover, the method and device of the invention can be realized by only making a minor modification to software in existing sleep sensors without incurring additional hardware costs.

Provided herein are methods and systems for monitoring a patient using a pressure-sensing device containing a pressure-sensitive region configured to selectively overlie a pressure ulcer-prone body part of the patient. In some embodiments, the pressure-sensing device includes a multilayered sensing unit containing a pressure-sensing layer to sense force and an adhesive layer configured to attach the pressure-sensing device to the body part. Also provided is a kit that includes the pressure-sensing device. The present methods, systems, and kits may find use in reducing the risk of pressure ulcer development in a patient.

A method includes affixing a co-planar sensor on a surface, using a processor, and providing an output. The method includes affixing a co-planar sensor on a surface at a first tissue site. The sensor has a force transducer disposed at an aperture of a rigid guard member. The guard member and the transducer are in co-planar alignment. The transducer is configured to provide an electrical signal corresponding to an internal pressure at the first tissue site. The method includes using the processor to compare the internal pressure with a reference value. Based on the comparison, the method includes providing an output corresponding to compartment syndrome at the first tissue site.

According to various, but not necessarily all, embodiments there is provided a respiratory volume measurement system. The respiratory volume measurement system comprises: at least one motion sensor and a machine learning system. The at least one motion sensor is configured to be placed on a chest wall of a subject to produce at least one sensor output signal dependent upon respiratory motion of the chest wall of the subject. The machine learning system is configured to receive at least the sensor output signal as an input and to produce at least a respiration measurement output, different to the sensor output signal, that provides at least a measure of respiration volume of the subject.

Disclosed is a technique of determining a driver's tension level (tension state degree) in vehicle driving in detail with a simple configuration. According to the technique, a nonlinear analyzing unit 110 of a driver's tension level determining apparatus 100 determining the tension level in driving of a driver acquires the driving operation amounts relating to driving operations of a driver (the operation amounts relating to operations of an accelerator pedal, a brake pedal, a handle, and the like), and then calculates the Lyapunov exponents about the driving operation amounts by performing nonlinear analysis processing. A frequency spectrum analyzing unit 120 calculates the power spectral density of time series data of the Lyapunov exponents, and then calculates an integrated value of a predetermined low frequency band in the calculated power spectral density. A driver's tension level determining unit 130 determines that the driver's tension level is any one of an excessive tension state, a moderate tension state, and an insufficient tension state using the integrated value of the predetermined low frequency band.

A pronunciation function evaluation system based on array high-density surface electromyography, this system includes a host computer and a slave computer, the slave computer is configured to obtain faciocervical electromyography signal through electromyography electrode arrays in a pronunciation process and to transmit the faciocervical electromyography signals to the host computer; the host computer is configured to analyze a physiological relevance between faciocervical array high-density electromyography signal features change and pronunciation function in the pronunciation process, to establish a three-dimensional dynamic energy distribution diagram of faciocervical muscular movement in the pronunciation process, to obtain dynamic visual temporal and spatial characteristic of articulatory muscles related to pronunciation, to extract electromyography features, to establish a faciocervical electromyography feature distribution standard database with normal pronunciation function, and to analyze a dysfunction condition and a damage degree of the pronunciation muscle group using a template matching and differentiation analysis algorithm.