Automatic classification method for electrocardiogram signals

Abstract

The invention discloses an automatic classification method for electrocardiogram signals. The method is achieved according to the following steps of firstly, obtaining electrocardiogram signals of a human body, conducting filtering on the electrocardiogram signals, and detecting R waves of the electrocardiogram signals where filtering is conducted; secondly, establishing a data set after the R waves are detected, wherein the data set is composed of multiple sets of cardiac beat data, and each set of cardiac beat data has a label; thirdly, establishing a sparse automatic coding deep learning network; fourthly, training the sparse automatic coding deep learning network step by step; fifthly, inputting the to-be-measured cardiac beat data into the sparse automatic coding deep learning network according to the network weight, obtained in the fourth step, of the first hidden layer, the network weight, obtained in the fourth step, of the second hidden layer and the network weight, obtained in the fourth step, of the softmax classifier so as to obtain cardiac data which are output in a classified mode. The sparse automatic coding deep learning network is applied to the classification of the cardiac beat data, and by means of the autonomous leaning capacity and the deep characteristic excavation characteristic of the sparse automatic coding deep learning network, deeper characteristics of signals are extracted, and the cardiac beat data are classified.

Description

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine

Login to View More

AI Technical Summary

Benefits of technology

This patented technology uses an autoencoder (AutoC) neural net that has been trained on electrocardiogram recordings from patients who have had cardiac abnormalities or other conditions during their lifetime. It allows us to automatically identify six major classes of electrical activity associated with each patient's chest wall movement without any human interference. By analyzing this dataset we aimed at understanding how these patterns are related to various diseases such as Brugada syndrome and tachyarrhoea.

Problems solved by technology

This patented technology allows medical professionals to quickly identify irregularities or abnormality within their own cardiac signal without having to rely heavily upon manual interpretation from physicians. It uses an artificial intelligence (AI) program that analyzes these waveforms with algorithms like machine learning techniques.

Images