A method to identify feature points associated with the heart valve movement, heart contraction or cardiac hemodynamics is revealed. The mechanocardiography (MCG) is a technology that makes use of vibrational waveforms acquired using at least one gravity sensor attached on one of the four heart valve auscultation sites on the body surface. The data of the electrocardiography (ECG) is recorded simultaneously with the MCG. The feature points are identified by comparing P, R and T points of synchronized ECG with the MCG spectrum. By the time sequences and amplitudes of the feature points, the method provides additional clinical information of cardiac cycle abnormalities for diagnosis.

According to an aspect, there is provided a device for measuring a physiological characteristic of a first subject, the device comprising a first electrode for contacting a part of the body of the first subject; a second electrode for contacting a part of the body of a second subject; and a control unit for obtaining an electrocardiogram, ECG, signal using the electrodes and for processing the ECG signal to determine a measurement of a physiological characteristic of the first subject; wherein the signal comprises a first ECG signal component relating to the first subject and a second ECG signal component relating to the second subject, and the control unit is configured to process the ECG signal obtained from the electrodes to extract the first ECG signal component relating to the first subject and to process the first ECG signal component to determine a measurement of the physiological characteristic of the first subject.

This disclosure relates generally to biomedical signal processing, and more particularly to method and system for physiological parameter derivation from pulsating signals with reduced error. In this method, pulsating signals are extracted, spurious perturbations in the extracted pulsating signals are removed for smoothening, local minima points in the smoothened pulsating signal are derived, systolic maxima point between two derived local minima are derived, most probable pulse duration and most probable peak-to-peak distance are derived, dicrotic minima is removed while ensuring that every dicrotic minima is preceded by a systolic maxima point and followed by a beat start point of said systolic maxima, diastolic peak is derived while ensuring that every dicrotic maxima is preceded by a diastolic notch followed by next beat start point of that maxima, and physiological parameters are derived from the derived local minima points, systolic maxima points, dicrotic notch and diastolic peak.

There is a method and system for detecting heartbeat irregularities comprising the steps of receiving a dataset representative of at least one waveform, the at least one waveform indicative of a subject's heart activity over a predetermined period of time; identifying from the data representative of at least one waveform, a plurality of peaks, each peak corresponding to a heartbeat; identifying from the predetermined period of time the time occurrence of each peak; calculating the difference (duration) between the time occurrence of each peak with its adjacent peak; determining the difference between each duration; classifying the absolute value of the difference into one of at least three intermediate categories; wherein each intermediate category comprises a specified range such that the absolute value is categorized into the intermediate category if it falls between the range; the intermediate categories further providing an indication of whether the subject has heartbeat irregularity.

An analysis system for analysis of the balance of circadian heart rhythm autonomic nervous system, the analysis system having tools which are designed to perform the following steps after artifact and arrhythmias removal from RR interval series: identification of the chaotic part of the RR interval series and analysis of the chaotic part; separation of the chaotic part from RR interval series and acquisition of the clean circadian RR series; acquisition of the normalized RR interval series using—interpolation, resampling and normalization; analysis of the heart rate bimodal distribution and modes of the normalized RR interval series; identification of the heart rate circadian period in the normalized RR data series; analysis of the heart rhythm variable part SNS and PNS regulation indicators in the normalized RR data series; generation of the report on obtained results in graph and tabular form.

A method and system for measuring psychological stress disclosed. In a first aspect, the method comprises determining R-R intervals from an electrocardiogram (ECG) to calculate a standard deviation of the R-R intervals (SDNN) and determining a stress feature (SF) using the SDNN. In response to reaching a threshold, the method includes performing adaptation to update a probability mass function (PMF). The method includes determining a stress level (SL) using the SF and the updated PMF to continuously measure the psychological stress. In a second aspect, the system comprises a wireless sensor device coupled to a user via at least one electrode, wherein the wireless sensor device includes a processor and a memory device coupled to the processor, wherein the memory device stores an application which, when executed by the processor, causes the processor to carry out the steps of the method.

A system for monitoring a subject for an arrhythmia includes an external monitoring device (EMD) configured to be disposed outside of a subject's body. The EMD includes a first communication component configured to receive, from a medical device, a first physiological parameter signal and an indication of a detected trigger event associated with a first portion of the first physiological parameter signal. The trigger event is indicative of a potential arrhythmia. The EMD also includes an analysis component configured to (1) identify a second portion of the first physiological parameter signal, where the second portion satisfies a discard criterion, (2) discard the second portion, and (3) perform an arrhythmia confirmation evaluation using a third portion of the first physiological parameter signal.

A cardiac rhythm management system includes at least one sensing component configured to obtain a first physiological parameter signal, an indication of a cardiac response to a stimulation therapy, and temporal information corresponding to the first physiological parameter signal and the cardiac response; and at least one processor configured to: receive the first physiological parameter signal, the indication of the cardiac response, and the temporal information; and to classify the cardiac response into a first cardiac response class to generate a classified cardiac response. The at least one processor also is configured to determine a correlation, based on the temporal information, between the first physiological parameter signal and the classified cardiac response.

A heartbeat detection device includes a difference value calculation unit (3) for calculating, from a sampling data sequence of an electrocardiographic waveform of a living body, one of an amount of change and a degree of change of sampling data for each sampling time, a multiplication unit (4) for calculating, for each sampling time, a product by multiplying one of an amount of change and a degree of change of the sampling data at a time K by one of the sampling data at the time K and sampling data at a time before the time K by a predetermined time t, a peak detection unit (5) for detecting a peak of the product, and a heartbeat time determination unit (6) for setting time of the peak of the product as a heartbeat time.

A medical device performs a method for detecting an atrial tachyarrhythmia by determining RR intervals between successive R-waves of a cardiac electrical signal and determining classification factors from the R-waves identified over a predetermined time period by determining at least a first classification factor correlated to variability of the RR intervals and a second classification factor indicating a presence of a ventricular tachyarrhythmia. The device classifies the cardiac electrical signal of the predetermined time period as unclassified, atrial tachyarrhythmia or non-atrial tachyarrhythmia by comparing the determined classification factors to classification criteria. The predetermined time period is classified as unclassified when the second classification factor indicates the presence of a ventricular tachyarrhythmia.