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Convolutional Neural Networks for Electrocardiogram Classification

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Abstract

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.

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Acknowledgements

The authors would like to extend their sincere appreciation to the Deanship of Scientific Research at King Saud University for its funding this Research group NO.(RG -1435-050).

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Authors and Affiliations

  1. College of Applied Computer Sciences, King Saud University, P. O. Box 51178, Riyadh, 11543, Saudi Arabia

    Mohamad M. Al Rahhal

  2. College of Computer and Information Sciences, King Saud University, P. O. Box 51178, Riyadh, 11543, Saudi Arabia

    Yakoub Bazi, Mansour Al Zuair & Esam Othman

  3. Raytheon Chair for Systems Engineering, Advanced Manufacturing Institute, King Saud University, P.O. Box 800, Riyadh, 11421, Saudi Arabia

    Bilel BenJdira

  4. Research Unit Signals and Mechatronic Systems SMS, National Engineering School of Carthage, Carthage University, UR13ES49, Tunis, Tunisia

    Bilel BenJdira

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  1. Mohamad M. Al Rahhal
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Correspondence to Mohamad M. Al Rahhal.

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Al Rahhal, M.M., Bazi, Y., Al Zuair, M. et al. Convolutional Neural Networks for Electrocardiogram Classification. J. Med. Biol. Eng. 38, 1014–1025 (2018). https://doi.org/10.1007/s40846-018-0389-7

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