Samsung Electronics Co., Ltd.

Yelei Li

Matthew C Wiggins

Asif Khalak

Anabel M De Proft

Xin Sui

As a non-limiting example, various aspects of this disclosure provide embodiments of real-time heartbeat events detection using low-power, low-noise motion sensor.

System and method for real-time heartbeat events detection using low-power motion sensor

A method of measuring biological signals of a person including obtaining physiological signals; converting the physiological signals to an energy entropy signal, determining one or more statistical features based on the energy entropy signal, and selecting and combining two or more of the features. The features include two or more of: Shannon entropy of power spectral density (PSD) of energy entropy; minimum envelopes of three axis signals; minimum Shannon entropy of PSD of three axis entropies; standard deviation of interbeat intervals (IBIs); differences of median filtered IBIs; determinism of recurrence plot of energy entropy; Kurtosis of energy entropy; minimum peak-to-peak amplitude three axis signals; peak-to-peak amplitude of energy entropy; laminarity of recurrence plot of energy entropy; Shannon entropy of horizontal structures in recurrence plot of energy entropy; standard deviation of time-averaged spectrogram; minimum standard deviation of peak positions in spectrogram; and minimum standard deviation of peak widths in spectrogram.

Feature selection for cardiac arrhythmia classification and screening

A user-wearable device utilizes energy harvesting technology to lengthen battery life or eliminates the need to charge the wearable device. In one embodiment, a user-wearable device combines energy harvesting technology with low power sensors and high efficiency processing methods to realize a self-charging or battery-less biometric monitoring system. The wearable biometric monitoring system provides accurate biometric measurements while enhancing user experience by extending the battery life or completely eliminating the need for the user to charge the device.

Self-powered wearable for continuous biometrics monitoring

A method for detecting arrhythmia, including receiving, via at least one motion sensor, channels of raw motion signals for a user; monitoring the channels for motion activity; generating segments from the raw motion signals; determining heartbeat event locations from the generated segments; performing false alarm detection on the raw motion signals and heartbeat event locations to generated refined abnormal candidates.

Systematic apparatus for motion sensor and optical sensor based cardiac arrhythmia triage

Provided is a system for measuring biological signals of a user, which includes a sensor module configured to acquire ballistocardiogram (BCG) signals of a user via a present channel, where the present channel is at least one channel of the sensor module, a decomposition module configured to decompose the BCG signals to decomposed signals, a reconstruction module configured to reconstruct at least a portion of the decomposed signals to reconstructed signals, a processing module configured to process the reconstructed signals to at least one of a heart rate, respiration rate, phases of respiration, and blood pressure, and a display module configured to display at least one output corresponding to the at least one of the heart rate, the respiration rate, phases of respiration, and the blood pressure on a display device.

Method and apparatus for heart rate and respiration rate estimation using low power sensor

As a non-limiting example, various aspects of this disclosure provide embodiments of continuous background heartrate and heartbeat events detection using a motion sensor during various phases of activity by a user.

System and method for continuous background heartrate and heartbeat events detection using a motion sensor

Provided is an electronic device to monitor a user's biological measurements, where a sensor is configured to acquire a raw signal from a user, and the electronic device determines a snoring signal from the raw signal by appropriately processing the raw signal.

System and method for snoring detection using low power motion sensor

A computer-implemented method for estimating biophysiological rates using the Hilbert transform includes receiving a quasiperiodic data stream from a biophysiological sensor, and removing at least a portion of an offset from the quasiperiodic data stream to provide a smoothed data stream by filtering the quasiperiodic data stream through a bandpass filter and phase compensating the filtered quasiperiodic data stream. The method also includes transforming the smoothed data stream into an analytic data stream using a Hilbert transform approximation and calculating the time derivative of the phase angle of the analytic data stream, where the time derivative is a frequency of the quasiperiodic data stream. The method further includes providing an output data stream derived from the frequency.

Methods for improving response time, robustness and user comfort in continuous estimation of biophysiological rates

Provided is an electronic device to monitor a user's biological measurements, where a sensor is configured to acquire a first signal from a user, and a diagnostic processor is configured to pre-process the first signal to generate a second signal, segment the second signal to form signal segments, determine at least one event location for each of the signal segments, match adjacent signal segments for feature alignment, and provide a third signal using results of the feature alignment.

System and method for providing a real-time signal segmentation and fiducial points alignment framework

A computer-implemented method for estimating a cardiovascular feature, such as heart interbeat intervals, using time-delay embedding includes receiving a quasiperiodic observational data stream from a biophysiological sensor, selecting a first delay value, and generating a first vector with a first dimensionality in a multidimensional coordinate space from the observational data stream through time-delay embedding. The method also includes selecting a first plane with a second dimensionality equal to one less than the first dimensionality and which passes through an origin of the coordinate space, identifying a plurality of crossings of the first vector through the first plane in one direction, and correlating a time value corresponding to each of the crossings. The method further includes calculating a plurality of periods between the time values corresponding to consecutive crossings, where the periods represent a stream of interbeat intervals.

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Santa Clara, CA, United States of America

Yelei Li