Deep convolutional neural network application to classify the ECG arrhythmia
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ORIGINAL PAPER
Deep convolutional neural network application to classify the ECG arrhythmia Fakheraldin Y. O. Abdalla1,2 · Longwen Wu1 · Hikmat Ullah1 · Guanghui Ren1 · Alam Noor1,3 · Hassan Mkindu1 · Yaqin Zhao1 Received: 2 August 2019 / Revised: 10 March 2020 / Accepted: 2 April 2020 © Springer-Verlag London Ltd., part of Springer Nature 2020
Abstract The ECG signal is such a substantial means to reflect all the electrical activities of the cardiac system. Therefore, it is considered by the physician as the essential tools and materials to diagnose and treat heart diseases. To deal with different types of arrhythmia, the physician manually inspects the ECG heartbeat. Since there are tiny alternations in the amplitude, durations and therefore the morphology, the computer-based systems were needed to develop such solutions in order to help the physician to do their job. In this study, a novel tactic to automatically classify ten different arrhythmia types was developed depending on the deep learning theory. Consequently, the well-known convolutional neural network (CNN) approach was adopted to classify those different types of arrhythmia. The structure of the proposed model consists of 11 layers distributed as follows: four layers as convolution interchanged with other four layers of max pooling and finally three successfully connected layers. The experiment was conducted with the dataset which was downloaded from the Physionet in the Massachusetts Institute of Technology-Beth Israel Hospital database and then augmented to get sufficient and balanced dataset. To evaluate the performance of the proposed method and compare it with the previous algorithms, confusion matrix, sensitivity (SEN), specificity (SPE), precision (PRE), area under curve and receiver operating characteristic have been used and calculated. It has been found that performance from the proposed method is better than the existing methods based on CNN, and the accuracy is 99.84. Keywords CNN · Arrhythmia classification · ECG signal
1 Introduction The structure of the heart has evolved to such an extent that it can function continuously throughout a human life without any failure. However, the World Health Organization reported that cardiovascular diseases are the number one cause of death globally. As assessed, 17.9 million people died
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from cardiovascular diseases (CVDs) in 2016, representing 31% of all global deaths. Furthermore, most of these deaths occurred in low-income countries. Almost half of the total deaths were classed as sudden cardiac deaths (SCDs), and arrhythmias are a reason for most of these diseases [1]. It is found that all the electrical activities of the heart are represented by the ECG signal and that manifested by P-
Longwen Wu [email protected]
Yaqin Zhao [email protected]
Fakheraldin Y. O. Abdalla [email protected]
1
Hikmat Ullah [email protected]
School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China
2
Guanghui Ren [email protected]
Co
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