Automatic Arrhythmia Detection Based on Convolutional Neural Networks

ECG signal is of great importance in the clinical diagnosis of various heart diseases. The abnormal origin or conduction of excitation is the electrophysiological mechanism leading to arrhythmia, but the type and frequency of arrhythmia is an important indicator reflecting the stability of cardiac electrical activity. In clinical practice, arrhythmic signals can be classified according to the origin of excitation, the frequency of excitation, or the transmission of excitation. Traditional heart disease diagnosis depends on doctors, and it is influenced by doctors' professional skills and the department's specialty. ECG signal has the characteristics of weak signal, low frequency, large variation, and easy to be interfered. In this investigation, an ECG anomaly automatic classification system based on the convolutional neural network is proposed. The training sets of the convolutional neural network are ECG beats extracted from the MIT-BIH database as training sets. A 36-layer convolutional neural network (CNN) is trained based on Caffe framework to classify ECG signals automatically. The experimental results show that it can reach or even exceed the level of a senior cardiologist in judging three diseases: FIB, AFL and IVR.