A computer implemented method for determining accuracy of heart rate variability is proposed. The method comprises the following steps: a) providing at least one photoplethysmogram obtained by at least one portable photoplethysmogram device (110); b) Determining at least one signal feature by evaluating the photoplethysmogram; c) Determining the accuracy of heart rate variability by using at least one trained model, wherein the signal features determined in step b) are used as input for the trained model.

Disclosed herein are methods that can comprise detecting a cardiopulmonary measurement in a subject. Disclosed herein are methods that can comprise monitoring a disease in a patient by detecting a cardiopulmonary measurement. Disclosed herein are methods that can comprise treating a patient after detecting a cardiopulmonary measurement in a subject.

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user obtained via a differential voltage potential measurement concerning the heart of the user; associating a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to the user.

A computer-implemented method, computer program product and computing system for receiving a single-lead heartbeat waveform for a user obtained via a differential voltage potential measurement concerning the heart of the user; associating a heart health indicator with the single-lead heartbeat waveform; and providing the heart health indicator to the user.

The present disclosure relates to a wearable monitor device and methods and systems for using such a device. In certain embodiments, the wearable monitor records cardiac data from a mammal and extracts particular features of interest. These features are then transmitted and used to provide health-related information about the mammal.

The present disclosure relates to a wearable monitor device and methods and systems for using such a device. In certain embodiments, the wearable monitor records cardiac data from a mammal and extracts particular features of interest. These features are then transmitted and used to provide health-related information about the mammal.

This application provides a method and apparatus of analyzing the ECG frequency parameters with applications for the diagnosis of ST-segment elevation myocardial infarction (STEMI) diseases, which relates to the interdisciplinary field of biomedical and science engineering. The method includes obtaining ECG signals from subjects through the designed electrodes; calculating ECG frequency domain parameters of the subjects based on the proposed power spectrum model and getting the analytical validation results after studying and verifying the parameters; generating indicators based on the analytical validation results, which could be potentially used as alternative indicators for STEMI diagnosis; and alerting when the indicators meet preset abnormal conditions. The present embodiment is a powerful tool to diagnose STEMI diseases faster and more effectively and helps patients receive timely assistance and treatment.

A system is provided for displaying heart graphic information relating to sources and source locations of a heart disorder to assist in evaluation of the heart disorder. A heart graphic display system provides an intra-cardiogram similarity (“ICS”) graphic and a source location (“SL”) graphic. The ICS graphic includes a grid with the x-axis and y-axis representing patient cycles of a patient cardiogram with the intersections of the patient cycle identifiers indicating similarity between the patient cycles. The SL graphic provides a representation of a heart with source locations indicated. The source locations are identified based on similarity of a patient cycle to library cycles of a library cardiogram of a library of cardiograms.

An aspect of the present invention is a loss-of-consciousness estimation apparatus including: a ventricular state estimation unit configured to estimate whether or not a ventricular state of an estimation target is normal in a predetermined repetition cycle, based on a cerebral blood flow correlation amount time series, which is a time series of an amount correlated with a cerebral blood flow rate of the estimation target; a measurement reliability estimation unit configured to estimate reliability of the cerebral blood flow correlation amount time series; and a loss-of-consciousness probability acquisition unit configured to, based on the reliability, the estimation result of the ventricular state estimation unit, and elapsed time after a probability acquisition start time, which is a time at which it is first determined by the ventricular state estimation unit that the ventricular state is not normal after an estimation start time, which is a time of starting the repetition cycle, acquire a loss-of-consciousness probability indicating a probability that the estimation target has already lost consciousness.

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).