An intelligent analysis method for supraventricular premature beats based on attention switching mechanism

Abstract

The invention provides an intelligent analysis method for supraventricular premature beats based on an attention conversion mechanism, which can improve the recognition accuracy of supraventricular premature beats, greatly save manual workload, and improve recognition efficiency at the same time. In the technical solution of this patent, all the stable heart rate segments of the RR interval are found based on the time RR interval scatter diagram, and all the stable heart rate segments of the RR interval are connected based on the forward and reverse propagation algorithm to obtain the corresponding ECG data to be identified Dominant heart rate curve; build a rhythm classification recognition model based on convolutional neural network, identify the rhythm type of each heartbeat, and judge the subject to be classified based on the dominant heart rate, RR interval deviation degree, rhythm type, and QRS wave shape of each heartbeat Beats for each type of supraventricular premature beat in the ECG data.

Description

technical field [0001] The invention relates to the technical field of medical artificial intelligence, in particular to an intelligent analysis method for supraventricular premature beats based on an attention conversion mechanism. Background technique [0002] Supraventricular premature beat refers to the premature contraction of the ectopic rhythm point originating above the ventricle, which is a common arrhythmia phenomenon in clinical practice. An electrocardiogram (ECG) is a graph formed by recording the changes in the electrical activity of the heart every cardiac cycle from the body surface. A variety of heart diseases in humans can be characterized by an electrocardiogram. Under normal circumstances, the number of heartbeats within 24 hours reaches 100,000. In the existing clinical diagnosis methods, it is necessary to find characteristic ECG waveform data in a large amount of dynamic ECG data, and then perform follow-up analysis and diagnosis based on these charac...

AI Technical Summary

Problems solved by technology

[0003] In order to solve the problem that the recognition error rate of supraventricular arrhythmia in the RR interval is relatively large in the existing automatic electrocardiogram analysis method, the present invention provides an intelligent analysis method for supraventricular premature beats based on the attention conversion mechanism, which It can improve the recognition accuracy of supraventricular premature beats, greatly save the manual workload, and improve the recognition efficiency at the same time

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