{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2023,12,15]],"date-time":"2023-12-15T00:43:44Z","timestamp":1702601024574},"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>With the development of smart healthcare, ECG monitoring has become an integral part of remote health care and plays a crucial role in diagnosing arrhythmias. However, the current mainstream ECG automatic diagnosis models lack research on incremental learning with accumulated personal data. Therefore, this paper proposes a personalized incremental learning method for diagnosing arrhythmias to facilitate the development of individualized models for personal users. Initially, the individual\u2019s ECG signals are encoded through ECG feature extractor composed of ResBlock, and Bi-LSTM. Subsequently, ECG diagnosis is performed using a personalized classifier tailored to the individual. As the personal data accumulates to a sufficient quantity, the personalized classifier is fine-tuned by incorporating the individual sample dataset with an arrhythmias-priority examplars based on herding, thus enabling the model to adapt to the individual domain. The experimental results demonstrate that the proposed model achieves an accuracy of 87.08% on the CPSC2018 dataset. Moreover, upon personalized incremental fine-tuning on the CPSC2020 dataset, the model\u2019s performance improves by over 13% compared to the initial model. Hence, the proposed personalized incremental learning method is effective.<\/jats:p>","DOI":"10.3233\/faia231099","type":"book-chapter","created":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:10:27Z","timestamp":1702566627000},"source":"Crossref","is-referenced-by-count":0,"title":["A Novel Personalized Incremental Arrhythmias Classification Method for ECG Monitoring"],"prefix":"10.3233","author":[{"given":"Zhiyuan","family":"Li","sequence":"first","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, 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"}]},{"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"}]},{"given":"Xiaoyang","family":"Wei","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, 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"}]},{"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"}]},{"given":"Liqun","family":"Zhao","sequence":"additional","affiliation":[{"name":"Department of cardiology, Shanghai First People\u2019s Hospital Affiliated to Shanghai Jiao Tong University, Shanghai 200080, China"}]},{"given":"Xiaoxue","family":"Yang","sequence":"additional","affiliation":[{"name":"Shanghai University of Finance and Economics, Shanghai 200433, 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\/FAIA231099","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,12,14]],"date-time":"2023-12-14T15:10:29Z","timestamp":1702566629000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA231099"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,12]]},"ISBN":["9781643684703","9781643684710"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia231099","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]]}}}