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Imbalanced Ectopic Beat Classification Using a Low-Memory-Usage CNN LMUEBCNet and Correlation-Based ECG Signal Oversampling

Author

Listed:
  • You-Liang Xie

    (Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan)

  • Che-Wei Lin

    (Department of Biomedical Engineering, College of Engineering, National Cheng Kung University, Tainan 701, Taiwan
    Medical Device Innovation Center, National Cheng Kung University, Tainan 701, Taiwan
    Institute of Gerontology, College of Medicine, National Cheng Kung University, Tainan 701, Taiwan
    Institute of Medical Informatics, College of Electrical Engineering and Computer Science, National Cheng Kung University, Tainan 701, Taiwan)

Abstract

Objective: This study presents a low-memory-usage ectopic beat classification convolutional neural network (CNN) (LMUEBCNet) and a correlation-based oversampling (Corr-OS) method for ectopic beat data augmentation. Methods : A LMUEBCNet classifier consists of four VGG-based convolution layers and two fully connected layers with the continuous wavelet transform (CWT) spectrogram of a QRS complex (0.712 s) segment as the input of the LMUEBCNet. A Corr-OS method augmented a synthetic beat using the top K correlation heartbeat of all mixed subjects for balancing the training set. This study validates data via a 10-fold cross-validation in the following three scenarios: training/testing with native data (CV1), training/testing with augmented data (CV2), and training with augmented data but testing with native data (CV3). Experiments : The PhysioNet MIT-BIH arrhythmia ECG database was used for verifying the proposed algorithm. This database consists of a total of 109,443 heartbeats categorized into five classes according to AAMI EC57: non-ectopic beats (N), supraventricular ectopic beats (S), ventricular ectopic beats (V), a fusion of ventricular and normal beats (F), and unknown beats (Q), with 90,586/2781/7236/803/8039 heartbeats, respectively. Three pre-trained CNNs: AlexNet/ResNet18/VGG19 were utilized in this study to compare the ectopic beat classification performance of the LMUEBCNet. The effectiveness of using Corr-OS data augmentation was determined by comparing (1) with/without using the Corr-OS method and (2) the Next-OS data augmentation method. Next-OS augmented the synthetic beat using the next heartbeat of one subject. Results : The proposed LMUEBCNet can achieve a 99.4% classification accuracy under the CV2 and CV3 cross-validation scenarios. The accuracy of the proposed LMUEBCNet is 0.4–0.5% less than the performance obtained from AlexNet/ResNet18/VGG19 under the same data augmentation and cross-validation scenario, but the parameter usage is only 10% or less than that of the AlexNet/ResNet18/VGG19 method. The proposed Corr-OS method can improve ectopic beat classification accuracy by 0.3%. Conclusion: This study developed a LMUEBCNet that can achieve a high ectopic beat classification accuracy with efficient parameter usage and utilized the Corr-OS method for balancing datasets to improve the classification performance.

Suggested Citation

  • You-Liang Xie & Che-Wei Lin, 2023. "Imbalanced Ectopic Beat Classification Using a Low-Memory-Usage CNN LMUEBCNet and Correlation-Based ECG Signal Oversampling," Mathematics, MDPI, vol. 11(8), pages 1-31, April.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:8:p:1833-:d:1121955
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    References listed on IDEAS

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    1. Ahmet Çınar & Seda Arslan Tuncer, 2021. "Classification of normal sinus rhythm, abnormal arrhythmia and congestive heart failure ECG signals using LSTM and hybrid CNN-SVM deep neural networks," Computer Methods in Biomechanics and Biomedical Engineering, Taylor & Francis Journals, vol. 24(2), pages 203-214, January.
    2. Qinghe Zheng & Mingqiang Yang & Xinyu Tian & Nan Jiang & Deqiang Wang, 2020. "A Full Stage Data Augmentation Method in Deep Convolutional Neural Network for Natural Image Classification," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-11, January.
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