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Open Access

RETRACTION

RETRACTED: Automatic Arrhythmia Detection Based on Convolutional Neural Networks

Zhong Liu1,2, Xinan Wang1,*, Kuntao Lu1, David Su3
Peking University Shenzhen Graduate School, Shenzhen, 518055, China.
School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China.
iFutureLab Inc. 955 Alma St., Suite B, CA 94301, Palo Alto.
* Corresponding Author: Xinan Wang. Email: .

Computers, Materials & Continua 2019, 60(2), 497-509. https://doi.org/10.32604/cmc.2019.04882

Abstract

ECG signal is of great importance in the clinical diagnosis of various heart diseases. The abnormal origin or conduction of excitation is the electrophysiological mechanism leading to arrhythmia, but the type and frequency of arrhythmia is an important indicator reflecting the stability of cardiac electrical activity. In clinical practice, arrhythmic signals can be classified according to the origin of excitation, the frequency of excitation, or the transmission of excitation. Traditional heart disease diagnosis depends on doctors, and it is influenced by doctors' professional skills and the department's specialty. ECG signal has the characteristics of weak signal, low frequency, large variation, and easy to be interfered. In this investigation, an ECG anomaly automatic classification system based on the convolutional neural network is proposed. The training sets of the convolutional neural network are ECG beats extracted from the MIT-BIH database as training sets. A 36-layer convolutional neural network (CNN) is trained based on Caffe framework to classify ECG signals automatically. The experimental results show that it can reach or even exceed the level of a senior cardiologist in judging three diseases: FIB, AFL and IVR.

Keywords

Convolutional neural network, ECG, arrhythmia detection, human, QRS

Cite This Article

Z. Liu, X. Wang, K. Lu and D. Su, "Retracted: automatic arrhythmia detection based on convolutional neural networks," Computers, Materials & Continua, vol. 60, no.2, pp. 497–509, 2019.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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