A biosignal measuring apparatus for converting and transmitting biosignals, includes a sensing unit configured to sense a user's electrocardiogram data, a communication unit configured to establish a wireless channel with a data receiving device, and a processing unit configured to determine real-time transmission characteristics based on communication quality information regarding the wireless channel, to determine a data resolution corresponding to the real-time transmission characteristics, to convert the electrocardiogram data according to the data resolution, and to transmit the electrocardiogram data to the data receiving device.

Provided are systems and methods for tracking one or more electrode positions.

The exemplified methods and systems facilitate the use for diagnostics, monitoring, treatment of one or more wavelet-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively. The wavelet-based features or parameters can be used, in one embodiment, within a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease or abnormal condition. Wavelet-based features or parameters may include measures that are derived from extractable properties or geometric characteristics of a spectral image or data of high-power spectral contents or high-coherence in waveform signals of interest in an acquired biophysical signal. Wavelet-based features or parameters may also include measures that are derived from a statistical quantification of the distribution of the power of the high-power spectral contents in the waveform signals of interest.

The exemplified methods and systems facilitate the use for diagnostics, monitoring, treatment of one or more wavelet-based features or parameters determined from biophysical signals such as cardiac/biopotential signals and/or photoplethysmography signals that are acquired non-invasively. The wavelet-based features or parameters can be used, in one embodiment, within a model or classifier (e.g., a machine-learned classifier) to estimate metrics associated with the physiological state of a subject, including for the presence or non-presence of a disease or abnormal condition. Wavelet-based features or parameters may include measures that are derived from extractable properties or geometric characteristics of a spectral image or data of high-power spectral contents or high-coherence in waveform signals of interest in an acquired biophysical signal. Wavelet-based features or parameters may also include measures that are derived from a statistical quantification of the distribution of the power of the high-power spectral contents in the waveform signals of interest.

A wearable medical monitoring system is provided that includes a support structure worn by a patient, which also housing electrodes and sleep sensors for generating health data. The health data includes ECG data, respiration data, and sleep data, which are used to determine cardiac conditions and sleep disorders. Sleep disorder indexes are formulated with various combinations of sleep factors from the health data of the patient, correlating in time with particular periods of sleep by the patient. In comparing the sleep disorder indexes with respective sleep disorder indicators, potential sleep disorders may be discerned by the monitoring system. Sleep information about the sleep disorders and warnings may be transmitted by the monitoring system to health support entities.

An automatic diagnostic apparatus and corresponding method is disclosed for recognizing heart sounds of interest, i.e., murmurs, detected in streaming audio data picked up by a stethoscope. Sensors included in the device capture audio data in real time during an auscultation exam performed by a physician. A feature vector that models the stream of audio data is created and supplied to a deep neural network stored on the diagnostic device. The deep neural network generates a probability for each of the heart sounds of interest. When the probability of detection exceeds a pre-established threshold value the device alerts the physician through visual and/or audio cues, enhancing the physician's diagnostic capability during routine examination.

An automatic diagnostic apparatus and corresponding method is disclosed for recognizing heart sounds of interest, i.e., murmurs, detected in streaming audio data picked up by a stethoscope. Sensors included in the device capture audio data in real time during an auscultation exam performed by a physician. A feature vector that models the stream of audio data is created and supplied to a deep neural network stored on the diagnostic device. The deep neural network generates a probability for each of the heart sounds of interest. When the probability of detection exceeds a pre-established threshold value the device alerts the physician through visual and/or audio cues, enhancing the physician's diagnostic capability during routine examination.

A single-impulse panoramic photoacoustic computed tomography (SIP-PACT) system for small-animal whole-body imaging is disclosed. In addition, a dual-speed of sound image universal back-projection reconstruction method is disclosed. Further, a PACT system for imaging a breast of a subject is disclosed.

A single-impulse panoramic photoacoustic computed tomography (SIP-PACT) system for small-animal whole-body imaging is disclosed. In addition, a dual-speed of sound image universal back-projection reconstruction method is disclosed. Further, a PACT system for imaging a breast of a subject is disclosed.

Systems and methods are described herein for selection of a cardiac cycle, or heartbeat, from a plurality of cardiac cycles monitored over time. The cardiac cycle may be selected using various metrics including a single-cycle metric and a cycle-series metric. Further, the selected cardiac cycle may be used for further cardiac analysis (for example, to generate electrical activation times).