Electrocardiogram recognition method based on multi-scale autoregression model

Abstract

The invention discloses an electrocardiogram recognition method based on a multi-scale autoregression model, which specifically comprises the following steps: step 1, acquiring an electrocardiosignal, and preprocessing the electrocardiosignal; step 2, respectively performing dynamic heart beat segmentation, sub-beat segmentation and multi-beat segmentation on the signals processed in the step 1; step 3, inputting the segmentation result obtained in the step 2 into a one-dimensional neural network to obtain an input feature map; and step 4, inputting the input feature map obtained in the step 3 into a channel attention module to obtain a channel attention map and improve the recognition accuracy of the classifier. According to the method, the one-dimensional neural network classifier and the channel attention module are used together, so that dual-aspect improvement of channel information extraction and channel information learning is realized.

Description

technical field [0001] The invention belongs to the technical field of shape recognition of electrocardiogram waveforms, and relates to an electrocardiogram recognition method based on a multi-scale autoregressive model. Background technique [0002] With the development of the times, our life has become more and more convenient, but with it comes the continuous increase of various pressures, so more and more people suffer from cardiovascular diseases. According to relevant reports from WHO, at the beginning of the last century, the number of deaths due to cardiovascular diseases accounted for less than 10% of the total deaths in the world, but after entering the 21st century, this proportion has risen to 50% in developed countries, and in developing countries National rose to 25%. According to statistics, nearly 20 million people die from cardiovascular diseases every year, and they are mainly distributed in developing countries. [0003] From the various reports above, w...

AI Technical Summary

Problems solved by technology

When segmenting heartbeats, the fixed-length segmentation method is used, which lacks dynamics, resulting in redundancy or lack of segmentation results, which is not conducive to feature extraction and classification, and reduces accuracy and performance; when extracting features, manual features and The depth features are not well combined, and the scale is relatively single. They are all based on a single heart beat, and sub-beats and multi-beats are rarely mentioned. improve

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