Combination of Convolutional and Recurrent Neural Networks for Heartbeat Classification

  • Abdelrahman M. ShakerEmail author
  • Manal TantawiEmail author
  • Howida A. ShedeedEmail author
  • Mohamed F. TolbaEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1153)


Electrocardiogram (ECG) plays an essential role in the medical field, it records the electrical activity of the heart over time and provides information about the heart condition. Hence, the cardiologist uses it to detect the abnormalities of the heart and to diagnose the heart diseases. Convolutional Neural Networks (CNNs) have proven their ability in extracting the most important features, Long Short-Term Memory (LSTM) has the capabilities of learning the temporal dependencies between the sequential data. In this paper, a novel method based on the combination of CNN and LSTM is proposed to classify 15 classes of the MIT-BIH dataset automatically without any hand-engineering feature extraction methods. The proposed method consists of data filtering, dynamic technique for heartbeat segmentation, and CNN-LSTM model consists of 12 layers.

Our experimental results of the proposed method achieved promising overall accuracy of 98.16% in classification between 15 classes of the MIT-BIH dataset, which outperforms several heartbeat classification methods.


ECG classification Heart diseases Deep learning Convolution Neural Networks (CNN) Long Short-Term Memory (LSTM) 


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Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Faculty of Computer and Information SciencesAin Shams UniversityCairoEgypt

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