A novel method for ECG signal classification via one-dimensional convolutional neural network
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A novel method for ECG signal classification via one‑dimensional convolutional neural network Xuan Hua1,2 · Jungang Han3 · Chen Zhao4 · Haipeng Tang5 · Zhuo He4 · Qinghui Chen6 · Shaojie Tang7 · Jinshan Tang4 · Weihua Zhou4 Received: 8 July 2020 / Accepted: 26 October 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract This paper develops an end-to-end ECG signal classification algorithm based on a novel segmentation strategy and 1D Convolutional Neural Networks (CNN) to aid the classification of ECG signals and alleviate the workload of physicians. The ECG segmentation strategy named R-R-R strategy (i.e., retaining ECG data between the R peaks just before and after the current R peak) is used for segmenting the original ECG data into segments to train and test the 1D CNN models. The novel strategy mimics physicians in scanning ECG to a greater extent, and maximizes the inherent information of ECG segments for diagnosis. The performance of the proposed end to end ECG signal classification algorithm was verified with the ECG signals from 48 records in the MIT-BIH arrhythmia database. When the heartbeat types were divided into the five classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, and paced beat, the classification accuracy, the area under the curve (AUC), the sensitivity, and the F1-score achieved by the proposed model were 0.9924, 0.9994, 0.99 and 0.99, respectively. When the heartbeat types were divided into six classes recommended by clinicians, i.e., normal beat, left bundle branch block beat, right bundle branch block beat, premature ventricular contraction, paced beat and other beats, the beat classification accuracy, the AUC, the sensitivity, and the F1-score achieved by the model reached 0.9702, 0.9966, 0.97, and 0.97, respectively. When the heartbeat types were divided into five classes recommended by the Association for Advancement of Medical Instrumentation (AAMI), i.e., normal beat, supraventricular ectopic beat, ventricular ectopic beat, fusion beat, and unknown beat, the beat classification accuracy, the sensitivity, and the F1-score were 0.9745, 0.97, and 0.97, respectively. Experimental results show that the proposed method achieves better performance than the state-of-the-art methods. Keywords Electrocardiogram · Convolutional neural networks · ECG signal classification · ECG segmentation strategy
* Jungang Han [email protected]
4
College of Computing, Michigan Technological University, Houghton, MI 49931, USA
* Jinshan Tang [email protected]
5
School of Computing, University of Southern Mississippi, Long Beach 39560, USA
* Weihua Zhou [email protected]
6
Department of Kinesiology and Integrative Physiology, Michigan Technological University, Houghton, MI 49931, USA
7
School of Automation, Xi’an University of Posts and Telecommunications, Xi’an 710121, China
1
College of Electronic Engineering, Xi’an University of Posts and Te
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