A method and a system for analyzing electrocardiographic segments previously derived from a cardiac device so as to help to discriminate true positives episodes, including abnormal heart rhythms, from false positives episodes, including normal heart rhythms. Each episode received includes at least one segment of electrocardiographic signal, and each segment is segmented into sub-segments. Score vectors are obtained for each sub-segment to classify the episode so as to discriminate true positive episodes from false positive episodes, and the classification results, which include at least the true positive episodes, are output.

A defibrillator (AED) and method for using a defibrillator using two different ECG analysis algorithms which work sequentially to improve the accuracy of AED shock decisions. A first algorithm, such as (ART), is particularly suited for analysis in the presence of CPR periods. A second algorithm, such as (PAS), is particularly suited for analysis during hands-off periods. The AED switches algorithms depending on the period and on the current analysis of the cardiac rhythm. The inventions thus provide an optimized ECG analysis scheme in a manner that improves the effectiveness of the rescue, resulting in more CPR “hands-on” time, better treatment of refibrillation, and reduced transition times between CPR and electrotherapy.

Disclosed relates to a method and an apparatus for analyzing an ECG signal, an electrophysiological signal recorder, a three-dimensional mapping system, and a computer device. The method includes: acquiring (S202) N first intracardiac electrical signals in a region of interest or at a reference point/in a reference region; generating (S204) a first comparison signal spectrogram according to the N first intracardiac waveforms; acquiring (S206) M second intracardiac electrical signals at the point of interest to acquire M second intracardiac waveforms; generating (S208) a second comparison signal spectrogram according to the M second intracardiac waveforms; performing (S210) a comparative analysis on the first comparison signal spectrogram and the second comparison signal spectrogram and generating a comparative analysis result; and outputting (S212) prompt information indicating whether the point of interest is a focal point according to the comparative analysis result.

Electrical impulses are received from a beating heart. The electrical impulses are converted to an ECG waveform. The ECG waveform is converted to a frequency domain waveform, which, in turn, is separated into two or more different frequency domain waveforms, which, in turn, are converted into a plurality of time domain cardiac electrophysiological subwaveforms and discontinuity points between these subwaveforms. The plurality of subwaveforms and discontinuity points are compared to a database of subwaveforms and discontinuity points for normal and abnormal patients or to a set of rules developed from the database. A bundle branches (BB) to J-Point (BB-J) interval is identified from the plurality of subwaveforms and discontinuity points based on the comparison. The ECG waveform with the BB-J interval annotated is displayed.

The invention provides a system and method for detecting a paced rhythm in a twelve lead ECG. A high-pass filter receives a signal from an ECG lead and pacer spikes within the high-pass filtered signal are distinguished from noise by setting certain threshold limits. Subsequently, clusters of pacer spikes are detected and raw pacer spikes corresponding to each cluster are determined. A pruning process is performed to identify one pacer spike corresponding to each paced beat and raw pacer spike with largest amplitude within a cluster is retained and other pacer spikes within the cluster are eliminated. Further, potential pacer spikes in the ECG lead are determined by eliminating pacer spikes in sections with missing data or high amount of noise. Further post-processing steps are performed to declare the identification of the paced rhythm in the ECG lead.

The invention provides a system and method for detecting a paced rhythm in a twelve lead ECG. A high-pass filter receives a signal from an ECG lead and pacer spikes within the high-pass filtered signal are distinguished from noise by setting certain threshold limits. Subsequently, clusters of pacer spikes are detected and raw pacer spikes corresponding to each cluster are determined. A pruning process is performed to identify one pacer spike corresponding to each paced beat and raw pacer spike with largest amplitude within a cluster is retained and other pacer spikes within the cluster are eliminated. Further, potential pacer spikes in the ECG lead are determined by eliminating pacer spikes in sections with missing data or high amount of noise. Further post-processing steps are performed to declare the identification of the paced rhythm in the ECG lead.

An apparatus for classifying stress includes an electrocardiogram (ECG) measurement module configured to measure an ECG signal in a first state, an idle state, and an ECG signal in a second state in which noise, having various magnitudes, is generated, a feature point extraction module configured to extract a feature point of each of the measured ECG signal in the first state and the measured ECG signal in the second state, and a clustering module configured to perform K-means clustering on the ECG signals in the first and second states based on the extracted feature points to classify stress.

A biological information measurement device which is capable of improving accuracy of detection of optimal exercise intensity, a biological information measurement method, and a biological information measurement program are provided. At the biological information measurement device, heart rate counting means measures an HR value indicating a heart rate on the basis of heart-rate data obtained by capturing heartbeats when a subject to be measured exercises, and analysis means performs power spectrum analysis on a heart rate variability frequency of the heart-rate data to calculate an LF value which is an integral value of low frequency components. Detection means then detects an HR/LF value by dividing the HR value with respect to exercise intensity of the subject to be measured by an LF value. Thus, by the biological information measurement device obtaining the HR/LF value as a new index indicating sympathetic nerve activity, it is possible to obtain biological information important for health management.

The present invention relates to a system and a method of determining disease based on a heat map image explainable from an electrocardiogram signal, which determine an electrocardiogram signal as a normal signal and a disease signal by transfer-learning a transfer-learning model through a deep learning network, calculate and visualize a part with a high relevance score to the determination to enable a user to objectively and finally determine the disease and the normal state. The system for determining disease based on a heat map image explainable from an electrocardiogram signal includes: an electrocardiogram measuring unit configured to acquire an electrocardiogram signal; a scalogram transform unit configured to transform the electrocardiogram signal acquired from the electrocardiogram measuring unit into a time-frequency region and store the transformed electrocardiogram signal as a two-dimensional image; a disease determining unit configured to determine the electrocardiogram signal as normal/disease through the two-dimensional image stored in the scalogram transform unit; a relevance score calculating unit configured to calculate a part contributed to determination of the electrocardiogram signal as normal/disease by the disease determining unit; and a heat map display unit configured to display the part contributed to the determination of the electrocardiogram signal as normal/disease calculated by the relevance score calculating unit as a heat map.

In one embodiment, a method to determine reliable electrocardiogram (ECG) signal is described. The method includes receiving at least one ECG signal for a period of time from a patient. The method also includes analyzing the at least one ECG signal to determine a first heart rate using a first method and analyzing the at least one ECG signal to determine a second heart rate using a second method different from the first method. The method includes comparing the first and second heart rates to each other and classifying the at least one ECG signal as reliable when a reliability threshold is satisfied.