Kutluana, GokhanTurker, Ilker2024-09-292024-09-2920241746-80941746-8108https://doi.org/10.1016/j.bspc.2023.105420https://hdl.handle.net/20.500.14619/4449As 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.eninfo:eu-repo/semantics/closedAccessECG ClassificationDeep LearningVisibility GraphComplex NetworksGraph RepresentationsArticle10.1016/j.bspc.2023.1054202-s2.0-85171466195Q187WOS:001082044400001Q1