Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques

Jia Li,Tao Xu,Saibiao Jiang,Yujuan Si

doi:10.1155/2018/7354081

Abstract

Although convolutional neural networks (CNNs) can be used to classify electrocardiogram (ECG) beats in the diagnosis of cardiovascular disease, ECG signals are typically processed as one-dimensional signals while CNNs are better suited to multidimensional pattern or image recognition applications. In this study, the morphology and rhythm of heartbeats are fused into a two-dimensional information vector for subsequent processing by CNNs that include adaptive learning rate and biased dropout methods. The results demonstrate that the proposed CNN model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction. When the proposed model was tested on the MIT-BIH arrhythmia database, the model achieved higher performance than other state-of-the-art methods for five and eight heartbeat categories (the average accuracy was 99.1% and 97%). In particular, the proposed system had better performance in terms of the sensitivity and positive predictive rate for V beats by more than 4.3% and 5.4%, respectively, and also for S beats by more than 22.6% and 25.9%, respectively, when compared to existing algorithms. It is anticipated that the proposed method will be suitable for implementation on portable devices for the e-home health monitoring of cardiovascular disease.

Highlights

The electrocardiogram (ECG) has become a useful tool [1, 2] for the diagnosis of cardiovascular diseases as it is fast and noninvasive
The results demonstrate that the proposed convolutional neural networks (CNNs) model is effective for detecting irregular heartbeats or arrhythmias via automatic feature extraction
The six most common heartbeat categories were selected from 46 recordings (ML II) of the MIT-BIH arrhythmia database for Experiment 1, namely, the normal beat (N), paced beat (/), atrial premature beat (A), premature ventricular contraction (V), left ventricular bundle branch block (L), and right bundle branch block beat (R) categories

Summary

Introduction

The electrocardiogram (ECG) has become a useful tool [1, 2] for the diagnosis of cardiovascular diseases as it is fast and noninvasive. While an experienced cardiologist can distinguish arrhythmias by visually referencing the morphological pattern of the ECG signals, a computeroriented approach can effectively reduce the diagnostic time and would enable the e-home health monitoring of cardiovascular disease [4]. Realizing such computer-oriented approaches remains challenging due to the time-varying dynamics and various profiles of ECG signals, which cause the classification precision to vary from patient to patient [5], as even for a healthy person, the morphological pattern of their ECG signals can vary significantly over a short time [6]. Machine learning methods, such as artificial neural networks (ANNs) [11], support vector machines (SVMs) [12], least squares support vector machines (LS-SVMs) [13], particle swarm optimization support vector machines (PSO-SVMs) [14], particle swarm optimization radial basis functions (PSO-RBFs) [15], and extreme learning machines (ELMs) [8], have been developed for the accurate classification of heartbeats

Methods

Results

Discussion

Conclusion