Clustering of derivative and integrative p-wave features for Bayès Syndrome detection

Lorena G. Franco; Luis A. Escobar; Antoni Bayès de Luna; José M. Massa

doi:10.1117/12.2606318

10 December 2021 Clustering of derivative and integrative p-wave features for Bayès Syndrome detection

Lorena G. Franco, Luis A. Escobar, Antoni Bayès de Luna, José M. Massa

Proceedings Volume 12088, 17th International Symposium on Medical Information Processing and Analysis; 120881B (2021) https://doi.org/10.1117/12.2606318
Event: Seventeenth International Symposium on Medical Information Processing and Analysis, 2021, Campinas, Brazil

Abstract

Early-stage detection of Bayès Syndrome, for preventive purposes, is of great interest due to its associations with multiple medical conditions. A strategy for increasing the original dataset employed on this syndrome's discovery is presented in this work. Furthermore, two clustering techniques were applied: K-Means++ (two different implementations) and FAUM. In addition, FAUM was used with a fixed number of clusters. By applying data augmentation techniques, 2113 signals were obtained from the 49 initial quantity. For each ECG signal sample, derivative and integrative variations on II, III and AVF leads, were calculated. On these datasets both K-Means++ (with Matlab and FAUM implementations) and FAUM (with a fixed number of clusters) were applied to these datasets. Regarding the results, K-Means++ with the Matlab implementation reached a value of 0.82 for the F1-Score metric. Also, integrative variation performance was better than derivative in detecting the P-Wave morphology.

Citation Download Citation

Lorena G. Franco, Luis A. Escobar, Antoni Bayès de Luna, and José M. Massa "Clustering of derivative and integrative p-wave features for Bayès Syndrome detection", Proc. SPIE 12088, 17th International Symposium on Medical Information Processing and Analysis, 120881B (10 December 2021); https://doi.org/10.1117/12.2606318

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