{"title":"基于注意力引导机制的心电图分类","authors":"","doi":"10.1016/j.cmpb.2024.108454","DOIUrl":null,"url":null,"abstract":"<div><h3>Background and Objective</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>","PeriodicalId":10624,"journal":{"name":"Computer methods and programs in biomedicine","volume":null,"pages":null},"PeriodicalIF":4.9000,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"ECG classification based on guided attention mechanism\",\"authors\":\"\",\"doi\":\"10.1016/j.cmpb.2024.108454\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><h3>Background and Objective</h3><div>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.</div></div><div><h3>Methods</h3><div>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.</div></div><div><h3>Results</h3><div>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.</div></div><div><h3>Conclusions</h3><div>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.</div></div>\",\"PeriodicalId\":10624,\"journal\":{\"name\":\"Computer methods and programs in biomedicine\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":4.9000,\"publicationDate\":\"2024-10-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Computer methods and programs in biomedicine\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0169260724004474\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Computer methods and programs in biomedicine","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0169260724004474","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
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.
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.