Convolutional Neural Networks for Electrocardiogram Classification

In this paper, we propose a transfer learning approach for Arrhythmia Detection and Classification in Cross ECG Databases. This approach relies on a deep convolutional neural network (CNN) pretrained on an auxiliary domain (called ImageNet) with very large labelled images coupled with an additional...

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Bibliographic Details
Published in:Journal of medical and biological engineering 2018-12, Vol.38 (6), p.1014-1025
Main Authors: Al Rahhal, Mohamad M., Bazi, Yakoub, Al Zuair, Mansour, Othman, Esam, BenJdira, Bilel
Format: Article
Language:English
Subjects:
Arrhythmia
Artificial neural networks
Biomedical Engineering and Bioengineering
Cardiac arrhythmia
Cell Biology
Classification
Color imagery
Continuous wavelet transform
EKG
Electrocardiography
Engineering
Entropy
Imaging
Neural networks
Original Article
Radiology
Regularization
Representations
State of the art
Transfer learning
Ventricle
Wavelet analysis
Wavelet transforms
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Summary:In this paper, we propose a transfer learning approach for Arrhythmia Detection and Classification in Cross ECG Databases. This approach relies on a deep convolutional neural network (CNN) pretrained on an auxiliary domain (called ImageNet) with very large labelled images coupled with an additional network composed of fully connected layers. As the pretrained CNN accepts only RGB images as the input, we apply continuous wavelet transform (CWT) to the ECG signals under analysis to generate an over-complete time–frequency representation. Then, we feed the resulting image-like representations as inputs into the pretrained CNN to generate the CNN features. Next, we train the additional fully connected network on the ECG labeled data represented by the CNN features in a supervised way by minimizing cross-entropy error with dropout regularization. The experiments reported in the MIT-BIH arrhythmia, the INCART and the SVDB databases show that the proposed method can achieve better results for the detection of ventricular ectopic beats (VEB) and supraventricular ectopic beats (SVEB) compared to state-of-the-art methods.
ISSN:1609-0985
2199-4757
DOI:10.1007/s40846-018-0389-7