Classification of cardiac disorders using weighted visibility graph features from ECG signals
Küçük Resim Yok
Tarih
2024
Yazarlar
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Elsevier Sci Ltd
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
As 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.
Açıklama
Anahtar Kelimeler
ECG Classification, Deep Learning, Visibility Graph, Complex Networks, Graph Representations
Kaynak
Biomedical Signal Processing and Control
WoS Q Değeri
Q1
Scopus Q Değeri
Q1
Cilt
87
