Automated Classification of Atrial Fibrillation Using Artificial Neural Network for Wearable Devices

Fengying Ma,Wei Liang,Jingyu Xue,Jingyao Zhang

doi:10.1155/2020/9159158

Abstract

Atrial fibrillation (AF), as one of the most common arrhythmia diseases in clinic, is a malignant threat to human health. However, AF is difficult to monitor in real time due to its intermittent nature. Wearable electrocardiogram (ECG) monitoring equipment has flourished in the context of telemedicine due to its real-time monitoring and simple operation in recent years, providing new ideas and methods for the detection of AF. In this paper, we propose a low computational cost classification model for robust detection of AF episodes in ECG signals, using RR intervals of the ECG signals and feeding them into artificial neural network (ANN) for classification, to compensate the defect of the computational complexity in traditional wearable ECG monitoring devices. In addition, we compared our proposed classifier with other popular classifiers. The model was trained and tested on the AF Termination Challenge Database and MIT-BIH Arrhythmia Database. Experimental results achieve the highest sensitivity of 99.3%, specificity of 97.4%, and accuracy of 98.3%, outperforming most of the others in the recent literature. Accordingly, we observe that ANN using RR intervals as an input feature can be a suitable candidate for automatic classification of AF.

Highlights

Atrial fibrillation (AF), a major cardiac arrhythmia abnormality in the clinic, is associated with substantial complications that threaten people’s health [1, 2], such as hypertension, diabetes, heart failure, and cardiovascular disease [3,4,5]. e reason that AF real-time monitoring has gained much attention is because AF causes a high mortality rate and because the duration of AF is relatively short to capture
Since AF happens unpredictably and due to the high requirement of timely treatment, more and more wearable devices that can analyze ECG signal are used for the real-time diagnosis of these subjects [12]
E results present that the artificial neural network (ANN) model we proposed is better than other models for the detection of AF. e contributions of this method include the following: (i) We propose an ANN model with lower computational complexity which has much reliable and higher classification accuracy than some of the models previously proposed for AF detection in wearable ECG monitoring devices

Summary

Introduction

Atrial fibrillation (AF), a major cardiac arrhythmia abnormality in the clinic, is associated with substantial complications that threaten people’s health [1, 2], such as hypertension, diabetes, heart failure, and cardiovascular disease [3,4,5]. e reason that AF real-time monitoring has gained much attention is because AF causes a high mortality rate and because the duration of AF is relatively short to capture. Since AF happens unpredictably and due to the high requirement of timely treatment, more and more wearable devices that can analyze ECG signal are used for the real-time diagnosis of these subjects [12]. Wearable ECG monitoring device as a real-time detection device can meet the requirement of collecting ECG signals and preliminary diagnosis by using the human sensor networks. It can promote the development of telemedicine. The classification algorithm for AF detection in real time that can be used in wearable ECG monitoring devices is significant

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Methods

Results