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This study compared several deep learning models for classifying cardiovascular disease using a single-lead ECG signal. The models included 1D-Convolutional Neural Network (CNN), 2D-CNN, Vision Transformer (ViT), and a hybrid model combining ResNet-34 with ViT. ECG signals were obtained from the MIT-BIH Arrhythmia database and preprocessed using SMOTE, wavelet denoising, and continuous wavelet transform. The 1D-CNN model used the denoised ECG signal as direct input. The 2D-CNN architecture used VGG-16 and ResNet-34. The hybrid model was proposed, which outperformed the other models, achieving an accuracy of 99.2%. The study highlights the importance of accurate diagnosis in improving patient health.

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Hybrid Deep Learning Approach for Accurate Cardiovascular Disease Detection Using Single-Lead ECG Signals

  • 会议论文

  • 发表日期：

    2025年

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  • Novie Theresia Br. Pasaribu,
  • Frederick Halim,
  • Kezia Sarahsophia Immanuel Ryadi,
  • Che-Wei Lin

摘要

This study compared several deep learning models for classifying cardiovascular disease using a single-lead ECG signal. The models included 1D-Convolutional Neural Network (CNN), 2D-CNN, Vision Transformer (ViT), and a hybrid model combining ResNet-34 with ViT. ECG signals were obtained from the MIT-BIH Arrhythmia database and preprocessed using SMOTE, wavelet denoising, and continuous wavelet transform. The 1D-CNN model used the denoised ECG signal as direct input. The 2D-CNN architecture used VGG-16 and ResNet-34. The hybrid model was proposed, which outperformed the other models, achieving an accuracy of 99.2%. The study highlights the importance of accurate diagnosis in improving patient health.