Classification of cardiac disorders using weighted visibility graph features from ECG signals

Basit Öğe Kaydı
dc.authoridKUTLUANA, GOKHAN/0000-0001-5004-8334
dc.contributor.authorKutluana, Gokhan
dc.contributor.authorTurker, Ilker
dc.date.accessioned2024-09-29T15:55:05Z
dc.date.available2024-09-29T15:55:05Z
dc.date.issued2024
dc.departmentKarabük Üniversitesien_US
dc.description.abstractAs universal expressions to describe complex systems, graphs are increasingly preferred as a representation method in artificial intelligence. Visibility graphs enable converting time-series data into graph representations, inheriting some key properties of the series. This study investigates the representation capacity of visibility graphs for ECG signals using either the sequence of node weights or the diagonals of the adjacency matrices as feature sets, input to ResNet and Inception classifier models. This approach also reduces the high dimensionality of the original graph representation which features a size of data points squared. Experiments performed on the multi-labeled PTB-XL dataset indicate that the first 3 diagonals of the visibility graph as the feature set to the ResNet model provides superior classification results compared to the original signal, node weights from the visibility graph, or the combinations of these inputs. Having achieved a maximum AUC score of 93.46%, this approach also outperforms the previously recorded ECG classification results for the PTB-XL dataset.en_US
dc.identifier.doi10.1016/j.bspc.2023.105420
dc.identifier.issn1746-8094
dc.identifier.issn1746-8108
dc.identifier.scopus2-s2.0-85171466195en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.bspc.2023.105420
dc.identifier.urihttps://hdl.handle.net/20.500.14619/4449
dc.identifier.volume87en_US
dc.identifier.wosWOS:001082044400001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofBiomedical Signal Processing and Controlen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectECG Classificationen_US
dc.subjectDeep Learningen_US
dc.subjectVisibility Graphen_US
dc.subjectComplex Networksen_US
dc.subjectGraph Representationsen_US
dc.titleClassification of cardiac disorders using weighted visibility graph features from ECG signalsen_US
dc.typeArticleen_US

Dosyalar

Koleksiyon

WoS İndeksli Yayınlar Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu