School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, 200093, People's Republic of China.
School of Basic Medical Science, Fudan University, Shanghai, 200032, People's Republic of China.
Physiol Meas. 2024 Apr 26;45(4). doi: 10.1088/1361-6579/ad3d25.
. Myocardial infarction (MI) is a serious cardiovascular disease that can cause irreversible damage to the heart, making early identification and treatment crucial. However, automatic MI detection and localization from an electrocardiogram (ECG) remain challenging. In this study, we propose two models, MFB-SENET and MFB-DMIL, for MI detection and localization, respectively.. The MFB-SENET model is designed to detect MI, while the MFB-DMIL model is designed to localize MI. The MI localization model employs a specialized attention mechanism to integrate multi-instance learning with domain knowledge. This approach incorporates handcrafted features and introduces a new loss function called lead-loss, to improve MI localization. Grad-CAM is employed to visualize the decision-making process.The proposed method was evaluated on the PTB and PTB-XL databases. Under the inter-patient scheme, the accuracy of MI detection and localization on the PTB database reached 93.88% and 67.17%, respectively. The accuracy of MI detection and localization on the PTB-XL database were 94.89% and 85.83%, respectively.. Our method achieved comparable or better performance than other state-of-the-art algorithms. The proposed method combined deep learning and medical domain knowledge, demonstrates effectiveness and reliability, holding promise as an efficient MI diagnostic tool to assist physicians in formulating accurate diagnoses.
心肌梗死（MI）是一种严重的心血管疾病，可导致心脏不可逆转的损伤，因此早期识别和治疗至关重要。然而，从心电图（ECG）中自动检测和定位 MI 仍然具有挑战性。在这项研究中，我们分别提出了用于 MI 检测和定位的两个模型，即 MFB-SENET 和 MFB-DMIL。MFB-SENET 模型旨在检测 MI，而 MFB-DMIL 模型旨在定位 MI。MI 定位模型采用专门的注意力机制，将多实例学习与领域知识相结合。这种方法结合了手工制作的特征，并引入了一种新的损失函数，称为导联损失，以提高 MI 定位的准确性。使用 Grad-CAM 可视化决策过程。该方法在 PTB 和 PTB-XL 数据库上进行了评估。在患者间方案下，PTB 数据库上 MI 检测和定位的准确率分别达到 93.88%和 67.17%。PTB-XL 数据库上 MI 检测和定位的准确率分别为 94.89%和 85.83%。我们的方法的性能与其他最先进的算法相当或更好。该方法结合了深度学习和医学领域知识，具有有效性和可靠性，有望成为一种有效的 MI 诊断工具，帮助医生做出准确的诊断。
