Electrocardiogram diagnosis using wavelet-based artificial neural network

Abstract

Electrocardiography (ECG) is a widely used noninvasive clinical tool for the diagnosis of cardiovascular disease. However, the accuracy of ECG analysis significantly affect the diagnostic error rate of cardiovascular diseases. Therefore, in recent year, many Neural Network (NN)-based approaches were proposed to automatically analyze the ECG signal. However, these methods suffer from long computing time, which is inappropriate for the mobile real-time application. To solve this problem, we propose a Wavelet-based Artificial Neural Network (W-ANN) diagnosis flow in this paper. Based on the wavelet transform, the W-ANN can provide not only cleaner ECG input signal but lower computing time. The experimental results show that the proposed method can reduce 49% computing time with only 11.7% ECG diagnostic accuracy loss by involving the data from MIT-BIH arrhythmia database and real ECG signal measurement.