{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:41:54Z","timestamp":1702600914897},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643684703","type":"print"},{"value":"9781643684710","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,12]],"date-time":"2023-12-12T00:00:00Z","timestamp":1702339200000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,12,12]]},"abstract":"<jats:p>Acute myocardial infarction (AMI) is a kind of heart disease with a high mortality rate and easy to ignore the early symptoms. Patients need to obtain timely and accurate diagnosis at the onset of the disease. Automatic ECG diagnosis based on deep learning method is significant for treating such patients because of its timeliness and ease of use. However, the \u201cblack box\u201d nature of deep learning methods affects the confidence of doctors and patients and seriously restricts the popularity of its clinical applications. Therefore, it is necessary to study its explainability. This paper uses a residual neural network to realize the end-to-end diagnosis of myocardial infarction disease in a 12-lead electrocardiogram. By designing a specific data processing method, the residual network is used to preserve the characteristics of the feature time order. With the time attention mechanism, we can realize the interpretability of the channel between 12 leads and the time interpretability of the signal segments in each lead. This study reveals the reliability of deep learning methods in clinical Settings.<\/jats:p>","DOI":"10.3233\/faia231108","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:10:41Z","timestamp":1702566641000},"source":"Crossref","is-referenced-by-count":0,"title":["An Interpretable Residual Neural Network for the Diagnosis of Myocardial Infarction"],"prefix":"10.3233","author":[{"given":"Xiaoyang","family":"Wei","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Mengxiao","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Yanrui","family":"Jin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Zhiyuan","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Yuanyuan","family":"Tian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China"}]},{"given":"Chengliang","family":"Liu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China"},{"name":"MoE Key Lab of Artificial Intelligence, AI Institute, Shanghai Jiao Tong University, Shanghai 200240, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining IX"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA231108","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:10:42Z","timestamp":1702566642000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231108"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"ISBN":["9781643684703","9781643684710"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231108","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,12]]}}}