Abstract

Info

Title:: Screening for myocardial infarction using convolutional neural network and long short-term memory network

Author(s):: LIU Jianhua; L?Jianfeng; CAI Jindan; Department of Cardiovascular Medicine, Renhe Hospital Affiliated to China Three Gorges University, Yichang 443000, China

Keywords:: Keywords: myocardial infarction electrocardiogram convolutional neural network long short-term memory network

Abstract:: Abstract: A screening method for myocardial infarction based on convolutional neural network and long short-term memory network is proposed to accurately detect myocardial infarction from electrocardiogram (ECG) signals, thereby further improving the diagnostic efficacy of myocardial infarction. Based on convolutional neural network, convolution neural network combined with long short-term memory network, and their integration, 3 different models are put forward to detect myocardial infarction and normal beats from ECG signals. In addition, the data resampling methods, namely synthetic minority oversampling technique and Tomek Link, are used to solve the class imbalance problem of data sets. The data resampled integrated convolutional neural network has obtained better experimental results than other methods, which proves the effectiveness of the proposed method.