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Combining the Taguchi Method and Convolutional Neural Networks for Arrhythmia Classification by Using ECG Images with Single Heartbeats

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Listed:
  • Shu-Fen Li

    (Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Mei-Ling Huang

    (Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

  • Yan-Sheng Wu

    (Department of Industrial Engineering & Management, National Chin-Yi University of Technology, Taichung 41170, Taiwan)

Abstract

In recent years, deep learning has been applied in numerous fields and has yielded excellent results. Convolutional neural networks (CNNs) have been used to analyze electrocardiography (ECG) data in biomedical engineering. This study combines the Taguchi method and CNNs for classifying ECG images from single heartbeats without feature extraction or signal conversion. All of the fifteen types (five classes) in the MIT-BIH Arrhythmia Dataset were included in this study. The classification accuracy achieved 96.79%, which is comparable to the state-of-the-art literature. The proposed model demonstrates effective and efficient performance in the identification of heartbeat diseases while minimizing misdiagnosis.

Suggested Citation

  • Shu-Fen Li & Mei-Ling Huang & Yan-Sheng Wu, 2023. "Combining the Taguchi Method and Convolutional Neural Networks for Arrhythmia Classification by Using ECG Images with Single Heartbeats," Mathematics, MDPI, vol. 11(13), pages 1-18, June.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:13:p:2841-:d:1178444
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    References listed on IDEAS

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    1. Rashedul Islam & Jia Uddin & Jong-Myon Kim, 2018. "Texture analysis based feature extraction using Gabor filter and SVD for reliable fault diagnosis of an induction motor," International Journal of Information Technology and Management, Inderscience Enterprises Ltd, vol. 17(1/2), pages 20-32.
    2. Jia Li & Yujuan Si & Tao Xu & Saibiao Jiang, 2018. "Deep Convolutional Neural Network Based ECG Classification System Using Information Fusion and One-Hot Encoding Techniques," Mathematical Problems in Engineering, Hindawi, vol. 2018, pages 1-10, December.
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