An ECG arrhythmia image classification system based on convolutional neural network

Abstract

The automatic classification of ECG images has great significance for doctors to diagnose cardiac diseases. In order to improve the accuracy and efficiency of disease diagnosis, this paper presents an automatic classification system for ECG arrhythmia images. The database of MIT-BIH is processed visually and a waveform detection method is proposed for detecting the QRS waveform. Wavelet transform is used to detect the R-R interval and locate the R point peak. A convolutional neural network(CNN) model was built to train and classify the ECG images. Experimental results show that according to the ANSI/AAMI EC57 evaluation criteria, the accuracy(Acc) rate of ventricular ectopic beat(VEB) can reach 95.9%. The sensitivity(Se) evaluation index is 93.0%. The specificity(Spe) evaluation index is 91.9%. For the supraventricular ectopic beat(SVEB) class, the accuracy rate is 93.2%, the sensitivity evaluation index is 81.3%, and the specificity evaluation index is 90.5%.