基于多实例学习和领域知识的多支心肌梗塞检测和定位框架

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
Xinyue Li, Yangcheng Huang, Yixin Ning, Mingjie Wang, Wenjie Cai
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引用次数: 0

摘要

目的 心肌梗塞(MI)是一种严重的心血管疾病,可对心脏造成不可逆的损伤,因此早期识别和治疗至关重要。然而,从心电图(ECG)中自动检测和定位心肌梗死仍然具有挑战性。在本研究中,我们提出了两种分别用于 MI 检测和定位的模型,即 MFB-SENET 和 MFB-DMIL。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--lead-loss,以改进 MI 定位。Grad-CAM 用于可视化决策过程。主要结果:在 PTB 和 PTB-XL 数据库上对所提出的方法进行了评估。在患者间方案下,PTB 数据库中 MI 检测和定位的准确率分别达到 93.88% 和 67.17%。在 PTB-XL 数据库中,MI 检测和定位的准确率分别为 94.89% 和 85.83%。所提出的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,有望成为一种高效的心肌梗死诊断工具,帮助医生做出准确诊断。.
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Multi-branch myocardial infarction detection and localization framework based on multi-instance learning and domain knowledge.
OBJECTIVE 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. APPROACH 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. Main Results: 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. SIGNIFICANCE 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. .
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
期刊介绍: ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.
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