Learning to Disentangle Inter-Subject Anatomical Variations in Electrocardiographic Data

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 2; pp. 860 - 870
• Main Authors: Gyawali, Prashnna K., Murkute, Jaideep Vitthal, Toloubidokhti, Maryam, Jiang, Xiajun, Horacek, B. Milan, Sapp, John L., Wang, Linwei
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.02.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Data models; Datasets; Disentangled representation learning; EKG; electrocardiograms; Electrocardiography; generative models; Geometry; Heart; Heart Ventricles; India Buffet process; Learning; Machine Learning; Representations; Simulation; Solid modeling; Task analysis; Torso; Variation; variational autoencoder; Ventricle

Objective: This work investigates the possibility of disentangled representation learning of inter-subject anatomical variations within electrocardiographic (ECG) data. Methods: Since ground truth anatomical factors are generally not known in clinical ECG for assessing the disentangling ability of t...