Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan.
Graduate Institute of Manufacturing Technology, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Intelligent Automation Engineering, National Taipei University of Technology, Taipei 10608, Taiwan; Department of Mechanical Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Comput Methods Programs Biomed. 2022 Jun;219:106762. doi: 10.1016/j.cmpb.2022.106762. Epub 2022 Mar 23.
The automatic recognition of myocardial infarction (MI) by artificial intelligence (AI) has been an emerging topic of academic research and an existing classification method that can recognize conventional electrocardiogram (ECG) signals with high accuracy. However, they are employed to classify one-dimensional (1-D) ECG signals rather than three-dimensional (3-D) ECG images, and it is limited to provide physicians with significant recommendations to aid in diagnosis like highlighting abnormal leads. Other studies on 3-D ECG images either did not achieve high accuracy or did not employ an inter-patient classification scheme. By proposing a multi-VGG deep neural network, this study aims to develop an automatic classification method for identifying myocardial infarction with inter-patient high accuracy and proper interpretability using 3-D ECG image and a Grad-CAM++ method.
We apply a multi-VGG deep convolutional neural network to top-view images of 3-D ECG, which are generated from simply denoised standard 12 leads ECG signals for classification. The multi-network method, which separately classifies QRS areas, ST areas, and whole heartbeats, was applied to improve classification performance. Furthermore, the Grad-CAM++ method was used to provide visually interpretable heatmaps for user's attention to improve network interpretability and assist physicians in MI diagnosis RESULTS: The proposed method achieved 95.65% inter-patient accuracy and exactly perfect inner-patient accuracy in the Physikalisch-Technische Bundesanstalt (PTB) diagnostic ECG database experiment. In the PTB-XL diagnostic ECG database, the proposed method achieved 97.23% inter-patient accuracy. The Grad-CAM++ experiment results also showed that the highlighted areas matched the medical diagnosis criteria for myocardial infarction.
Our method demonstrates that 3-D ECG images with AI classification can be efficiently employed for heart disease diagnosis with both high accuracy and visual interpretability.
人工智能（AI）自动识别心肌梗死（MI）一直是学术研究的新兴课题，现有的分类方法可以高精度识别常规心电图（ECG）信号。然而，它们用于分类一维（1-D）ECG 信号，而不是三维（3-D）ECG 图像，并且仅限于为医生提供有助于诊断的重要建议，例如突出显示异常导联。其他关于 3-D ECG 图像的研究要么没有达到高精度，要么没有采用患者间分类方案。通过提出一种多 VGG 深度神经网络，本研究旨在开发一种自动分类方法，使用 3-D ECG 图像和 Grad-CAM++方法，通过多患者高精度和适当的可解释性识别心肌梗死。
我们将多 VGG 深度卷积神经网络应用于 3-D ECG 的顶视图图像，这些图像是从简单去噪的标准 12 导联 ECG 信号生成的，用于分类。多网络方法分别对 QRS 区域、ST 区域和整个心跳进行分类，以提高分类性能。此外，还使用 Grad-CAM++方法提供可视可解释的热图，以提高网络的可解释性并帮助医生进行 MI 诊断。
在 Physikalisch-Technische Bundesanstalt（PTB）诊断 ECG 数据库实验中，所提出的方法在患者间达到了 95.65%的准确率，并且在患者内达到了完美的准确率。在 PTB-XL 诊断 ECG 数据库中，所提出的方法在患者间达到了 97.23%的准确率。Grad-CAM++实验结果还表明，突出显示的区域与心肌梗死的医学诊断标准相匹配。
我们的方法表明，使用 AI 分类的 3-D ECG 图像可以高效地用于心脏病诊断，具有高精度和可视可解释性。
