A visually interpretable detection method combines 3-D ECG with a multi-VGG neural network for myocardial infarction identification. (June 2022)

• Record Type: Journal Article
• Authors: Fang, Rui; Lu, Chih-Cheng; Chuang, Cheng-Ta; Chang, Wen-Han
• Is Part Of: Computer methods and programs in biomedicine. Volume 219(2022)
• Publication Date: 2022-06
• Subjects: Myocardial infarction -- Electrocardiogram -- 3-D ECG image -- Convolutional neural network -- Visual interpretability; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2022.106762
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: A visually interpretable method combines 3-D ECG with deep neural network for MI identification. Multi-DNN architecture improves classification accuracy in both PTB and PTB-XL database. Grad-CAM++ experiment boosts network visual interpretability for MI patients. Promising work offers efficacy in assisting physicians for heart disease diagnosis. Abstract: Background and Objective: 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. Methods: 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. Conclusions: 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.