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
• DOI: 10.1109/TBME.2021.3108164
• ISSN: 0018-9294; 1558-2531

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 the models, the presented work first proposes the SimECG data set, a 12-lead ECG data set procedurally generated with a controlled set of anatomical generative factors. Second, to perform such disentanglement, the presented method evaluates and compares deep generative models with latent density modeled by nonparametric Indian Buffet Process to account for the complex generative process of ECG data. Results: In the simulated data, the experiments demonstrate, for the first time, concrete evidence of the possibility to disentangle key generative anatomical factors within ECG data in separation from task-relevant generative factors. We achieve a disentanglement score of 92.1% while disentangling five anatomical generative factors and the task-relevant generative factor. In both simulated and real-data experiments, this work further provides quantitative evidence for the benefit of disentanglement learning on the downstream clinical task of localizing the origin of ventricular activation. Overall, the presented method achieves an improvement of around 18.5%, and 11.3% for the simulated dataset, and around 7.2%, and 3.6% for the real dataset, over baseline CNN, and standard generative model, respectively. Conclusion: These results demonstrate the importance as well as the feasibility of the disentangled representation learning of inter-subject anatomical variations within ECG data. Significance: This work suggests the important research direction to deal with the well-known challenge posed by the presence of significant inter-subject variations during an automated analysis of ECG data.