Research On Anomaly Prediction In ECG Signals Based On Deep Learning

Cardiovascular disease has always been one of the main causes of death in the world,which is a serious threat to human health.With the continuous development of modern medical technology,ECG signal processing has gradually developed into a hot research direction.As a noninvasive and transthoracic diagnostic technology,ECG has been widely used to monitor heartbeat activity.In view of the increasing demand for ECG data analysis,most domestic research remains in the stage of classification and processing,and the prediction of ECG abnormalities is relatively few.However,the early warning of ECG abnormality is very essential to prevent the coming possible danger.With the continuous development of machine learning and deep learning,it has been more and more widely used in the field of medicine.More and more neural networks are used for the detection and analysis of biological signals.In view of the above situation,based on the traditional ECG analysis and processing,and combined with the deep learning model,this paper carries out research for the purpose of predicting the possible abnormalities of ECG signal in the future.The main research contents are as follows:(1)Based on the LSTM network model,the prediction model of stacked LSTM network is designed to deepen the network and predict more complex problems.On this basis,the attention mechanism is introduced to focus on the key target areas.It can obtain more detailed information about the targets that need attention and reduce the input of other irrelevant information.The final classification accuracy of the model is 96.8%.(2)The prediction model of spiking neural network is designed.The neuron structure of pulse neural network is more similar to that of actual organisms,and the influence of time information on the actual situation is considered.Similarly,attention mechanism is introduced into the model.The model can better understand the characteristics of ECG signals and give corresponding weight distribution according to their importance,so as to improve the ability of ECG feature extraction and screening.The final classification accuracy of the model is 98.8%The ECG anomaly prediction model designed in this paper is tested on MIT-BIH data set.The test results show that the two ECG anomaly prediction algorithms based on attention mechanism designed in this paper have achieved good results in the accuracy of ECG prediction and recognition,which fully proves the superiority of their performance.