Introduced here are computer programs and associated computer-implemented techniques for deriving insights into the health of patients through analysis of audio data generated by electronic stethoscope systems. A diagnostic platform may be responsible for examining the audio data generated by an electronic stethoscope system so as to gain insights into the health of a patient. The diagnostic platform may employ heuristics, algorithms, or models that rely on machine learning or artificial intelligence to perform auscultation in a manner that significantly outperforms traditional approaches that rely on visual analysis by a healthcare professional.

A seed laser is configured to emit seed laser light. A plurality of optical amplifiers is configured to generate amplified laser light by amplifying the seed laser light. Each of the optical amplifiers is configured to separately direct its respective amplified laser light to a medium without being optically combined within the laser assembly with any of the other amplified laser light emitted by other optical amplifiers in the plurality of optical amplifiers.

A method for counting coughs is provided. The method includes acquiring a plurality of onset signals from the sound signal, wherein the onset signal has a predetermined time length; acquiring a plurality of spectrograms corresponding to each of the plurality of onset signals; determining whether each of the acquired plurality of spectrograms represents a cough using a cough determination model; and calculating a number of coughs included in the sound signal based on a time point of a cough signal. The cough signal is an onset signal corresponding to one spectrogram determined to represent the cough. When a time interval between a first time point of a first cough signal and a second time point of a second cough is within a reference time interval, the first cough signal and the second cough signal are regarded as one cough signal at the first time point.

A method of determining a measure of contact of an EMG sensor with the skin of a human or animal subject, the method comprising: receiving data captured by the EMG sensor; calculating a spectral entropy of the received data over a first time period in respect of a predetermined frequency band; determining a measure of contact for the first time period in dependence on the spectral entropy of the received data; and processing the received data of the first time period in dependence on the measure of contact.

The exemplified methods and systems facilitate the use, for diagnostics, monitoring, treatment, of one or more power spectral-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively from surface sensors placed on a patient while the patient is at rest. The power spectral-based features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.

A clinical evaluation system and method are disclosed that facilitate the use of one or more conduction deviation features or parameters determined from biophysical signals such as cardiac or biopotentials signals. Conduction derivation features or parameters may include VD conduction derivation features or parameters and/or VD conduction derivation Poincaré features or parameters. The conduction derivation features or parameters can be used in a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a patient, including for the presence or non-presence of a disease, a medical condition, or an indication of either. The estimated metric may be used to assist a physician or other healthcare provider in diagnosing the presence or non-presence and/or severity and/or localization of diseases or conditions or in the treatment of said diseases or conditions.

A pulse sensor is capable of measuring a pulse rate of a wearer at a peripheral artery. In an embodiment, the pulse sensor includes a magnet supported to move responsive to an arterial pulse and a magnetometer configured to detect changes in a magnetic field produced by the magnet. The magnet may include a plurality of ferromagnetic particles disposed in or on a flexible substrate configured to be held adjacent to human skin subject to arterial palpation and a magnetic sensor configured to sense movement of the ferromagnetic particles. A system and method may measure hydration includes using a pulse sensor to measure pulse rate and modulation. The wearer is prompted when the pulse rate and pulse modulation indicate a response to dehydration of the wearer.

Exemplified method and system facilitates monitoring and/or evaluation of disease or physiological state using mathematical analysis and machine learning analysis of a biopotential signal collected from a single electrode. The exemplified method and system creates, from data of a singularly measured biopotential signal, via a mathematical operation (i.e., via numeric fractional derivative calculation of the signal in the frequency domain), one or more mathematically-derived biopotential signals (e.g., virtual biopotential signals) that is used in combination with the measured biopotential signals to generate a multi-dimensional phase-space representation of the body (e.g., the heart). By mathematically modulating (e.g., by expanding or contracting) portions of a given biopotential signal, in the frequency domain, the numeric-based operation gives emphasis or de-emphasis to certain measured frequencies of the biopotential signals, which, when coupled with machine learning, facilitates improved diagnostics of certain pathologies.

A system and method for camera-based stress determination. The method includes: determining a plurality of regions-of-interest (ROIs) of a body part; determining a set of bitplanes in a captured image sequence for each ROI that represent HC changes using a trained machine learning model, the machine learning model trained with a hemoglobin concentration (HC) changes training set, the HC changes training set trained using bitplanes from previously captured image sequences of other human individuals as input and received cardiovascular data as targets; determining an HC change signal for each of the ROIs based on changes in the set of determined bitplanes; for each ROI, determining intervals between heartbeats based on peaks in the HC change signal; determining heart rate variability using the intervals between heartbeats; determining a stress level using at least one determination of a standard deviation of the heart rate variability; and outputting the stress level.

A computer implemented method and system is provided for managing neural stimulation therapy. The method comprises under control of one or more processors configured with program instructions. The method delivers a series of candidate stimulation waveforms having varied stimulation intensities to at least one electrode located proximate to nervous tissue of interest. A parameter defines the candidate stimulation waveforms is changed to vary the stimulation intensity. The method identifies a first candidate stimulation waveform that induces a paresthesia-abatement effect, while continuing to induce a select analgesic effect. The method further identifies a second candidate stimulation waveform that does not induce the select analgesic effect. The method sets a stimulation therapy based on the first and second candidate stimulation waveforms.