A knowledge-driven graph convolutional network for abnormal electrocardiogram diagnosis

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dc.authoridAlhudhaif, Adi/0000-0002-7201-6963
dc.authoridHe, Ziyang/0000-0003-3286-7138
dc.contributor.authorGe, Zhaoyang
dc.contributor.authorCheng, Huiqing
dc.contributor.authorTong, Zhuang
dc.contributor.authorHe, Ziyang
dc.contributor.authorAlhudhaif, Adi
dc.contributor.authorPolat, Kemal
dc.contributor.authorXu, Mingliang
dc.date.accessioned2024-09-25T19:57:43Z
dc.date.available2024-09-25T19:57:43Z
dc.date.issued2024
dc.departmentAbant İzzet Baysal Üniversitesien_US
dc.description.abstractThe electrocardiogram (ECG) signal comprising P-, Q-, R-, S-, and T -waves is an indispensable noninvasive diagnostic tool for analyzing physiological conditions of the heart. In general, traditional ECG intelligent diagnosis methods gradually extract features of the signal from input data until they can classify the ECG signal. However, the decision -making process of ECG intelligence models is implicit to clinicians. Clinical experts rely on clear and specific features extracted from ECG data to diagnose cardiac diseases effectively. Inspired by this clinical diagnosis mechanism, we propose an ECG knowledge graph (ECG -KG) framework primarily to improve ECG classification by presenting knowledge of ECG clinical diagnosis. In particular, the ECG -KG framework contains an ECG semantic feature extraction module, a knowledge graph construction module, and an ECG classification module. First, the ECG semantic feature extraction module locates the key points using the difference value method and further calculates the ECG attribute features. Further, the knowledge graph construction module utilizes attribute features to design entities and relationships for constructing abnormal ECG triples. The triples vectorize ECG abnormalities through the strategy of knowledge graph embedding strategy. Finally, the ECG classification module combines the ECG knowledge graph with the graph convolutional network model and adequately integrates expert knowledge to identify ECG abnormalities. Experiments conducted on the benchmark QT, the CPSC-2018, and the ZZU-ECG datasets show that the ECG -KG framework considerably outperforms other ECG diagnosis models, indicating the effectiveness of the ECG -KG framework for ECG abnormality diagnosis.en_US
dc.description.sponsorshipKey Scientific Research Project in Colleges and Universities of Henan Province of China [24A520043]; Department of Science and Technology of Henan Province [242102210096]en_US
dc.description.sponsorshipThis work is supported in part by the Key Scientific Research Project in Colleges and Universities of Henan Province of China under Grant No. 24A520043, in part by the Department of Science and Technology of Henan Province under Grant No. 242102210096.en_US
dc.identifier.doi10.1016/j.knosys.2024.111906
dc.identifier.issn0950-7051
dc.identifier.issn1872-7409
dc.identifier.scopus2-s2.0-85193635946en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.knosys.2024.111906
dc.identifier.urihttps://hdl.handle.net/20.500.12491/13567
dc.identifier.volume296en_US
dc.identifier.wosWOS:001244252400001en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofKnowledge-Based Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmzYK_20240925en_US
dc.subjectElectrocardiography (ECG)en_US
dc.subjectKnowledge graphen_US
dc.subjectGraph convolutional network (GCN)en_US
dc.subjectECG abnormality diagnosisen_US
dc.titleA knowledge-driven graph convolutional network for abnormal electrocardiogram diagnosisen_US
dc.typeArticleen_US

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