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Improved ECG heartbeat classification based on 1-D convolutional neural networks

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Abstract

ECG (Electrocardiogram) waves have a significant role in identifying the nature of heart diseases and monitoring the heart situation of patients who suffer from different cardiovascular diseases. In this research, a model that implements a binary classification in the light of the 1D-CNN algorithm is proposed for the ECG signal environment. The main objective of this study is to detect and separate regular and irregular heartbeat signals. The model has been trained and tested on the MIT-BIH dataset and classifies the signal into "normal" and "abnormal" samples using various activation functions for different epochs. Test results indicated that, according to the confusion matrix, an improvement of 99.8%, 99.9%, and 99.7% was achieved for accuracy, sensitivity, and specificity, respectively. These results are better than other related works that have been recently published for the classification of ECG signals.

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Data availability

The dataset has taken from this website.

https://www.physionet.org/content/mitdb/1.0.0/

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Authors and Affiliations

  1. Computer Science Department, College of Science, University of Halabja, Halabja, KRG, Iraq

    Ayub Othman Abdulrahman, Karwan Mahdi Hama Rawf & Aree Ali Mohammed

  2. Computer Science Department, College of Science, University of Sulaimani, Sulaimani, KRG, Iraq

    Aree Ali Mohammed

Authors
  1. Ayub Othman Abdulrahman
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  2. Karwan Mahdi Hama Rawf
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  3. Aree Ali Mohammed
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Correspondence to Ayub Othman Abdulrahman.

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Abdulrahman, A.O., Hama Rawf, K.M. & Mohammed, A.A. Improved ECG heartbeat classification based on 1-D convolutional neural networks. Multimed Tools Appl (2023). https://doi.org/10.1007/s11042-023-17619-5

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  • DOI: https://doi.org/10.1007/s11042-023-17619-5

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