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ECG based one-dimensional residual deep convolutional auto-encoder model for heart disease classification

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Abstract

The timely prediction of heart diseases with an automated system reduces the mortality rate of cardiac disease patients. However, detecting cardiac disease is one of the difficult tasks due to the small variations in the ECG signal that cannot be easily visible to the human eyes. To overcome this issue, many techniques have been introduced to effectively classify the variation in beats. However, those techniques face high error and fail to learn the spatiotemporal features, which badly affects the accuracy performance. Hence, a novel hybridized DL technique is introduced, which analyzes the spatio-temporal features and performs the heartbeat classification accurately with less error rate. At the initial stage, the signal from the raw dataset is smoothened to enhance the accuracy performance. The pre-processed samples are then balanced using the synthetic minority oversampling (SMOTE) technique to avoid over-fitting issues. Then, spatiotemporal features are extracted using a novel hybridized DL based One-Dimensional Residual Deep Convolutional Auto-Encoder (1D-RDCAE) technique. Finally, ML based extreme gradient boosting (XGB) classifier is introduced to classify the ECG heartbeats effectively. The proposed model is implemented via PYTHON and processed with the MIT-BIH arrhythmia dataset. Performance measures like accuracy, sensitivity, specificity, and false negative rate are analyzed and compared with existing techniques. In the experimental section, the proposed model obtains an accuracy of 99.9% and a specificity of 99.8%. Compared to other existing models, the proposed model shows better outcomes. Consequently, clinical cardiac care systems may benefit from this strategy as well.

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Authors and Affiliations

  1. Department of Computer Science & Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Guntur-Dt, Andhra Pradesh, India

    Nikhat Parveen & Md Shamsul Haque Ansari

  2. University of Technology and Applied Science, Muscat, Oman

    Manisha Gupta

  3. Vignana Bharathi Institute of Technology, Hyderabad, Telangana, India

    Shirisha Kasireddy

  4. Department of Computer Science, King Khalid University, Abha, 61421, Saudi Arabia

    Mohammad Nadeem Ahmed

Authors
  1. Nikhat Parveen
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  2. Manisha Gupta
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  3. Shirisha Kasireddy
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  4. Md Shamsul Haque Ansari
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  5. Mohammad Nadeem Ahmed
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Correspondence to Nikhat Parveen.

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Parveen, N., Gupta, M., Kasireddy, S. et al. ECG based one-dimensional residual deep convolutional auto-encoder model for heart disease classification. Multimed Tools Appl 83, 66107–66133 (2024). https://doi.org/10.1007/s11042-023-18009-7

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  • DOI: https://doi.org/10.1007/s11042-023-18009-7

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