Electrocardiosignal classifying method and system based on LRF-ELM and BLSTM

Abstract

The invention provides an electrocardiosignal classifying method and system based on LRF-ELM and BLSTM. The electrocardiosignal classifying method includes the steps: acquiring electrocardiosignal data, preprocessing the electrocardiosignal data to obtain a dataset, and using the electrocardiosignal data in the dataset as input data of a neural network; using a LRF-ELM network as a feature extractor, learning spatial information in the electrocardiosignal data, and through three stacked random convolution and pooling processes, extracting feature data of different dimensions in the electrocardiosignal data; and after fusion, using the extracted feature data as input of a sequence learning stage, adopting a deep BLSTM network to carry out sequence learning, and finally outputting electrocardiosignal classifying results. According to the electrocardiosignal classifying method and system based on the LRF-ELM and the BLSTM, time information and spatial information of electrocardiosignals are taken into account at the same time, and therefore, not only can electrocardiosignal features be extracted efficiently and rapidly, but also good classification and identification properties are ensured.

Description

technical field [0001] The present disclosure relates to the technical field of ECG signal classification, in particular to a method and system for ECG signal classification based on LRF-ELM and BLSTM. Background technique [0002] The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art. [0003] Electrocardiography is a technique in which hospitals use an electrocardiogram machine to connect to the body surface to measure the electrical activity generated by the beating of the human heart and reflect it on the image. A complete heartbeat mainly includes P wave, QRS wave and T wave. Because there is a potential difference between the inside and outside of the myocardial cell membrane, when the myocardial cells are depolarized sequentially from the endocardium to the epicardium, positive ions enter the membrane from the outside of the membrane, making the potential in the membrane cha...

AI Technical Summary

Problems solved by technology

[0005] The inventors of the present disclosure found that the current research on the recognition of ECG signals has the following problems: (1) manual extraction of features requires a certain amount of experience and knowledge, and there are many human interventions, which are likely to cause problems such as data loss; (2) ECG data sets are often Large amount, using a single deep learning algorithm consumes a lot of memory and requires high computer hardware configuration; (3) Based on computationally intensive deep learning methods, the calculation is complex and the amount of calculation is large; (4) Most deep learning algorithms need to be iterated Tuning the parameters, inappropriate parameter selection will affect the accuracy of feature extraction, thereby affecting the classification effect

Therefore, feature extraction using deep learning often requires a long training time

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