QRS wave group detection method and device based on feature template matching, medium and equipment

Abstract

The invention discloses a QRS wave group detection method and device based on feature template matching, a medium and equipment, and belongs to the technical field of electrocardio. The method comprises steps of firstly, preprocessing electrocardiosignals, and using the preprocessed signals for feature conversion; selecting a reliable classical detection algorithm to carry out strict threshold screening on the QRS wave group, so that the false alarm rate of the QRS wave group is extremely low; using the screened QRS wave group to make a multi-class feature template; screening a QRS wave group to be matched by using a classic detection algorithm for reducing a threshold value, and keeping an extremely low missing report rate; and finally, performing similarity matching calculation on the candidate QRS wave groups by using a multi-class feature template, performing threshold division, and re-sampling the QRS wave groups in a result to an original frequency. Compared with the prior art, the method of combining multiple types of QRS wave group templates and multiple waveform features is adopted, and the detection speed is effectively improved while high detection accuracy is kept.

Description

technical field [0001] The invention belongs to the technical field of electrocardiology, and in particular relates to a QRS wave group detection method, device, medium and equipment based on feature template matching. Background technique [0002] In the field of medical and health care, cardiovascular disease, as a common chronic and sudden-onset disease, has been plagued by patients and medical workers. For most chronic cardiovascular diseases, there is currently no medical technology that can completely cure them. Since cardiovascular diseases are mostly chronic diseases and have a high probability of sudden onset, the ideal way is to provide round-the-clock medical monitoring for patients, but ordinary patients cannot afford the high cost of long-term monitoring in hospitals, and such The disease is a chronic disease, and the signs of patients are exactly the same as those of healthy people when they are not ill. If long-term hospital monitoring is adopted, it is actual...

AI Technical Summary

Problems solved by technology

However, there are still some defects in this scheme: first, the selection of the template is too single, and cannot take into account both normal and abnormal QRS waves; second, the selected template features are single, and cannot effectively represent the characteristics of the signal; Sliding window comparison is performed on all signals, and the calculation amount is relatively high

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