Detection of atrial fibrillation from variable-duration ECG signal based on time-adaptive densely network and feature enhancement strategy

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dc.authorid0000-0002-6214-1429en_US
dc.authorid0000-0003-1840-9958en_US
dc.authorid0000-0002-7201-6963en_US
dc.authorid0000-0002-6091-1880en_US
dc.authorid0000-0001-7594-1487en_US
dc.contributor.authorZhang, Xianbin
dc.contributor.authorJiang, Mingzhe
dc.contributor.authorPolat, Kemal
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorHemanth, Jude
dc.contributor.authorWu, Wanqing
dc.date.accessioned2023-08-28T12:35:43Z
dc.date.available2023-08-28T12:35:43Z
dc.date.issued2023en_US
dc.departmentBAİBÜ, Mühendislik Fakültesi, Elektrik Elektronik Mühendisliği Bölümüen_US
dc.descriptionThis work was supported in part by the National Natural Science Foundation of China under Grant 61873349, in part by the General Logistics Department of PLA under Grant BLB19J005, and in part by Guangzhou Science and Technology Planning Project under Grant 202003000040.en_US
dc.description.abstractAtrial fibrillation (AF) is one of the clinic's most common arrhythmias with high morbidity and mortality. Developing an intelligent auxiliary diagnostic model of AF based on a body surface electrocardiogram (ECG) is necessary. Convolutional neural network (CNN) is one of the most commonly used models for AF recognition. However, typical CNN is not compatible with variable-duration ECG, so it is hard to demonstrate its universality and generalization in practical applications. Hence, this paper proposes a novel Time-adaptive densely network named MP-DLNet-F. The MP-DLNet module solves the problem of incompatibility between variable-duration ECG and 1D-CNN. In addition, the feature enhancement module and data imbalance processing module are respectively used to enhance the perception of temporal-quality information and decrease the sensitivity to data imbalance. The experimental results indicate that the proposed MP-DLNet-F achieved 87.98% classification accuracy, and F1-score of 0.847 on the CinC2017 database for 10-second cropped/padded single-lead ECG fragments. Furthermore, we deploy transfer learning techniques to test heterogeneous datasets, and in the CPSC2018 12-lead dataset, the method improved the average accuracy and F1-score by 21.81% and 16.14%, respectively. Experimental results indicate that our method can update the constructed model's parameters and precisely forecast AF with different duration distributions and lead distributions. Combining these advantages, MP-DLNet-F can exemplify all kinds of varied-duration or imbalance medical signal processing problems such as Electroencephalogram (EEG) and Photoplethysmography (PPG).en_US
dc.description.sponsorshipNational Natural Science Foundation of China [61873349]; General Logistics Department of PLA [BLB19J005]; Guangzhou Science and Technology Planning Project [202003000040]en_US
dc.identifier.citationZhang, X., Jiang, M., Polat, K., Alhudhaif, A., Hemanth, J., & Wu, W. (2022). Detection of Atrial Fibrillation from Variable-Duration ECG Signal Based on Time-Adaptive Densely Network and Feature Enhancement Strategy. IEEE Journal of Biomedical and Health Informatics, 27(2), 944-955.en_US
dc.identifier.doi10.1109/JBHI.2022.3221464
dc.identifier.endpage955en_US
dc.identifier.issn2168-2194
dc.identifier.issn2168-2208
dc.identifier.issue2en_US
dc.identifier.pmid36367916en_US
dc.identifier.scopus2-s2.0-85141592121en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.startpage944en_US
dc.identifier.urihttps://hdl.handle.net/20.500.12491/11604
dc.identifier.volume27en_US
dc.identifier.wosWOS:000965615000001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorPolat, Kemal
dc.language.isoenen_US
dc.publisherIEEE-Institute Electrical Electronics Engineers Incen_US
dc.relation.ispartofIEEE Journal of Biomedical and Health Informaticsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectElectrocardiographyen_US
dc.subjectFeature Extractionen_US
dc.subjectConvolutional Neural Networksen_US
dc.subjectArrhythmia Detectionen_US
dc.subjectRobust-Detectionen_US
dc.subjectClassificationen_US
dc.titleDetection of atrial fibrillation from variable-duration ECG signal based on time-adaptive densely network and feature enhancement strategyen_US
dc.typeArticleen_US

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