基于注意力引导机制的心电图分类

IF 4.9 2区 医学 Q1 COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
Yangcheng Huang , Wenjing Liu , Ziyi Yin , Shuaicong Hu , Mingjie Wang , Wenjie Cai
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引用次数: 0

摘要

背景和目的:将领域知识整合到深度学习模型中可以提高模型的有效性和可解释性。本研究旨在根据不同心脏异常的特点定制特定的引导机制,从而提高心电图(ECG)的分类性能:方法:引入了两种新颖的引导注意机制,即空间引导注意机制(GSA)和基于 CAM 的空间引导注意机制(CGAM)。根据临床知识为四种心电图异常分类任务创建了不同的注意力引导标签:ST变化检测、早搏识别、沃尔夫-帕金森-怀特综合征(WPW)分类和心房颤动(AF)检测。每个分类任务都分别对模型进行了训练和评估。使用 Shapley 值对模型的可解释性进行量化:在 ST 变化检测、早搏识别、WPW 分类和房颤检测方面,GSA 模型的 F1 分数分别提高了 5.74%、5%、8.96% 和 3.91%。同样,CGAM 在相应任务中的得分也分别提高了 3.89%、5.40%、8.21% 和 1.80%。结合使用 GSA 和 CGAM 后,改进幅度更大,分别为 6.26%、5.58%、8.85% 和 4.03%。此外,当同时执行所有四项任务时,整体性能也得到了显著提升,这证明了所提出模型的广泛适应性。量化的夏普利值证明了引导注意力机制在提高模型可解释性方面的有效性:结论:利用领域知识的引导注意力机制有效地引导了模型的注意力,从而提高了分类性能和可解释性。这些发现对促进准确的自动心电图分类具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ECG classification based on guided attention mechanism

Background and Objective

Integrating domain knowledge into deep learning models can improve their effectiveness and increase explainability. This study aims to enhance the classification performance of electrocardiograms (ECGs) by customizing specific guided mechanisms based on the characteristics of different cardiac abnormalities.

Methods

Two novel guided attention mechanisms, Guided Spatial Attention (GSA) and CAM-based spatial guided attention mechanism (CGAM), were introduced. Different attention guidance labels were created based on clinical knowledge for four ECG abnormality classification tasks: ST change detection, premature contraction identification, Wolf-Parkinson-White syndrome (WPW) classification, and atrial fibrillation (AF) detection. The models were trained and evaluated separately for each classification task. Model explainability was quantified using Shapley values.

Results

GSA improved the F1 score of the model by 5.74%, 5%, 8.96%, and 3.91% for ST change detection, premature contraction identification, WPW classification, and AF detection, respectively. Similarly, CGAM exhibited improvements of 3.89%, 5.40%, 8.21%, and 1.80% for the respective tasks. The combined use of GSA and CGAM resulted in even higher improvements of 6.26%, 5.58%, 8.85%, and 4.03%, respectively. Moreover, when all four tasks were conducted simultaneously, a notable overall performance boost was achieved, demonstrating the broad adaptability of the proposed model. The quantified Shapley values demonstrated the effectiveness of the guided attention mechanisms in enhancing the model's explainability.

Conclusions

The guided attention mechanisms, utilizing domain knowledge, effectively directed the model's attention, leading to improved classification performance and explainability. These findings have significant implications in facilitating accurate automated ECG classification.
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来源期刊
Computer methods and programs in biomedicine
Computer methods and programs in biomedicine 工程技术-工程:生物医学
CiteScore
12.30
自引率
6.60%
发文量
601
审稿时长
135 days
期刊介绍: To encourage the development of formal computing methods, and their application in biomedical research and medical practice, by illustration of fundamental principles in biomedical informatics research; to stimulate basic research into application software design; to report the state of research of biomedical information processing projects; to report new computer methodologies applied in biomedical areas; the eventual distribution of demonstrable software to avoid duplication of effort; to provide a forum for discussion and improvement of existing software; to optimize contact between national organizations and regional user groups by promoting an international exchange of information on formal methods, standards and software in biomedicine. Computer Methods and Programs in Biomedicine covers computing methodology and software systems derived from computing science for implementation in all aspects of biomedical research and medical practice. It is designed to serve: biochemists; biologists; geneticists; immunologists; neuroscientists; pharmacologists; toxicologists; clinicians; epidemiologists; psychiatrists; psychologists; cardiologists; chemists; (radio)physicists; computer scientists; programmers and systems analysts; biomedical, clinical, electrical and other engineers; teachers of medical informatics and users of educational software.
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