{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,1]],"date-time":"2025-10-01T16:22:43Z","timestamp":1759335763868,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":22,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,8,13]],"date-time":"2024-08-13T00:00:00Z","timestamp":1723507200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,8,13]]},"DOI":"10.1145\/3706890.3706956","type":"proceedings-article","created":{"date-parts":[[2025,1,13]],"date-time":"2025-01-13T13:37:20Z","timestamp":1736775440000},"page":"378-384","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["A generalization model for arrhythmia classification based on Spectral feature extraction and domain generalization"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-8022-2475","authenticated-orcid":false,"given":"Zhenpan","family":"Liu","sequence":"first","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7517-330X","authenticated-orcid":false,"given":"Xiang","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science and Technology, Wuhan University of Science and Technology, Wuhan, Hubei, China"}]}],"member":"320","published-online":{"date-parts":[[2025,1,13]]},"reference":[{"issue":"003","key":"e_1_3_3_1_1_2","first-page":"209","article-title":"\u201cChina Cardiovascular Disease Report 2018[J]","volume":"034","author":"Hu S","year":"2019","unstructured":"Hu S, Yang Y., Zheng Z, et al. Summary of \u201cChina Cardiovascular Disease Report 2018[J]. Chinese Journal of Circulation, 2019, 034(003):209-220.","journal-title":"Chinese Journal of Circulation"},{"key":"e_1_3_3_1_2_2","volume-title":"Cardiovascular disease as a leading cause of death: how are pharmacists getting involved?[J]. Integrated pharmacy research & practice","author":"Mc Namara K","year":"2019","unstructured":"Mc Namara K, Alzubaidi H, Jackson J K. Cardiovascular disease as a leading cause of death: how are pharmacists getting involved?[J]. Integrated pharmacy research & practice, 2019, 8: 1."},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2020.101817]"},{"key":"e_1_3_3_1_4_2","first-page":"5","article-title":"Research progress on the pathogenesis of arrhythmias[J]","volume":"02","author":"Yang B","year":"2010","unstructured":"Yang B, Cai B. Research progress on the pathogenesis of arrhythmias[J]. International Journal of Pharmaceutical Research, 2010, (02):5-12.","journal-title":"International Journal of Pharmaceutical Research"},{"key":"e_1_3_3_1_5_2","volume-title":"ECG interpretation skill acquisition: A review of learning, teaching and assessment[J]. Journal of electrocardiology","author":"Breen C J","year":"2022","unstructured":"Breen C J, Kelly G P, Kernohan W G. ECG interpretation skill acquisition: A review of learning, teaching and assessment[J]. Journal of electrocardiology, 2022, 73: 125-128."},{"volume-title":"ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network[J]","year":"2019","key":"e_1_3_3_1_6_2","unstructured":"HUANG J, CHEN B, YAO B, et al. ECG arrhythmia classification using STFT-based spectrogram and convolutional neural network[J]. IEEE access, 2019, 7: 92871-92880."},{"volume-title":"Classification of ECG signal using FFT based improved Alexnet classifier[J]. PLOS one","year":"2022","key":"e_1_3_3_1_7_2","unstructured":"KUMAR M A, CHAKRAPANI A. Classification of ECG signal using FFT based improved Alexnet classifier[J]. PLOS one, 2022, 17(9): e0274225."},{"volume-title":"How transferable are features in deep neural networks[J]. arXiv: Learning,arXiv: Learning","year":"2014","key":"e_1_3_3_1_8_2","unstructured":"YOSINSKI J, CLUNE J, BENGIO Y, et al. How transferable are features in deep neural networks[J]. arXiv: Learning,arXiv: Learning, 2014."},{"key":"e_1_3_3_1_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics9010121"},{"key":"e_1_3_3_1_10_2","volume-title":"Conrad T O F. Transfer learning for ECG classification[J]. Scientific reports","author":"Weimann K","year":"2021","unstructured":"Weimann K, Conrad T O F. Transfer learning for ECG classification[J]. Scientific reports, 2021, 11(1): 5251."},{"key":"e_1_3_3_1_11_2","volume-title":"Self-supervised learning of pretext-invariant representations[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition","author":"Misra I","year":"2020","unstructured":"Misra I, Maaten L. Self-supervised learning of pretext-invariant representations[C]\/\/Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition. 2020, 6707-6717."},{"key":"e_1_3_3_1_12_2","volume-title":"Self-supervised representation learning from 12-lead ECG data[J]. Computers in biology and medicine","author":"Mehari T","year":"2022","unstructured":"Mehari T, Strodthoff N. Self-supervised representation learning from 12-lead ECG data[J]. Computers in biology and medicine, 2022, 141: 105114."},{"key":"e_1_3_3_1_13_2","first-page":"1","article-title":"Listen2yourheart: A self-supervised approach for detecting murmur in heart-beat sounds[C]\/\/2022 Computing in Cardiology (CinC)","volume":"498","author":"Ballas A","year":"2022","unstructured":"Ballas A, Papapanagiotou V, Delopoulos A, et al. Listen2yourheart: A self-supervised approach for detecting murmur in heart-beat sounds[C]\/\/2022 Computing in Cardiology (CinC). IEEE, 2022, 498: 1-4.","journal-title":"IEEE"},{"key":"e_1_3_3_1_14_2","doi-asserted-by":"publisher","DOI":"10.1109\/TAFFC.2020.3014842"},{"key":"e_1_3_3_1_15_2","volume-title":"Attention is all you need[J]. Advances in neural information processing systems","author":"Vaswani A","year":"2017","unstructured":"Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need[J]. Advances in neural information processing systems, 2017, 30."},{"key":"e_1_3_3_1_16_2","first-page":"1","volume-title":"IEEE","author":"Natarajan A","year":"2020","unstructured":"Natarajan A, Chang Y, Mariani S, et al. A wide and deep transformer neural network for 12-lead ECG classification[C]\/\/2020 Computing in Cardiology. IEEE, 2020, 1-4."},{"key":"e_1_3_3_1_17_2","volume-title":"Generalizing to unseen domains: A survey on domain generalization[J]","author":"Wang J","year":"2022","unstructured":"Wang J, Lan C, Liu C, et al. Generalizing to unseen domains: A survey on domain generalization[J]. IEEE transactions on knowledge and data engineering, 2022, 35(8): 8052-8072."},{"key":"e_1_3_3_1_18_2","first-page":"1","article-title":"Deep discriminative domain generalization with adversarial feature learning for classifying ECG signals[C]\/\/2021 Computing in Cardiology (CinC)","volume":"48","author":"Shang Z","year":"2021","unstructured":"Shang Z, Zhao Z, Fang H, et al. Deep discriminative domain generalization with adversarial feature learning for classifying ECG signals[C]\/\/2021 Computing in Cardiology (CinC). IEEE, 2021, 48: 1-4.","journal-title":"IEEE"},{"key":"e_1_3_3_1_19_2","doi-asserted-by":"publisher","DOI":"10.1088\/1361-6579\/acb30f"},{"key":"e_1_3_3_1_20_2","volume-title":"Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification[J]. Philosophical Transactions of the Royal society A","author":"Lai C","year":"2021","unstructured":"Lai C, Zhou S, Trayanova N A. Optimal ECG-lead selection increases generalizability of deep learning on ECG abnormality classification[J]. Philosophical Transactions of the Royal society A, 2021, 379(2212): 20200258."},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Ballas A Diou C. A domain generalization approach for out-of-distribution 12-lead ecg classification with convolutional neural networks[C]\/\/2022 IEEE Eighth International Conference on Big Data Computing Service and Applications (BigDataService). IEEE 2022 9-13.","DOI":"10.1109\/BigDataService55688.2022.00009"},{"key":"e_1_3_3_1_22_2","volume-title":"Generalization challenges in ECG deep learning: Insights from dataset characteristics and attention mechanism[J]. medRxiv","author":"Huang Z","year":"2023","unstructured":"Huang Z, MacLachlan S, Yu L, et al. Generalization challenges in ECG deep learning: Insights from dataset characteristics and attention mechanism[J]. medRxiv, 2023, 2023.07. 05.23292238."}],"event":{"name":"ISAIMS 2024: 2024 5th International Symposium on Artificial Intelligence for Medicine Science","acronym":"ISAIMS 2024","location":"Amsterdam Netherlands"},"container-title":["Proceedings of the 2024 5th International Symposium on Artificial Intelligence for Medicine Science"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706890.3706956","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3706890.3706956","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T01:17:19Z","timestamp":1750295839000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3706890.3706956"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,13]]},"references-count":22,"alternative-id":["10.1145\/3706890.3706956","10.1145\/3706890"],"URL":"https:\/\/doi.org\/10.1145\/3706890.3706956","relation":{},"subject":[],"published":{"date-parts":[[2024,8,13]]},"assertion":[{"value":"2025-01-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}