Toward ECG-based analysis of hypertrophic cardiomyopathy: a novel ECG segmentation method for handling abnormalities. (3rd August 2022)

• Record Type: Journal Article
• Authors: Nezamabadi, Kasra; Mayfield, Jacob; Li, Pengyuan; Greenland, Gabriela V; Rodriguez, Sebastian; Simsek, Bahadir; Mousavi, Parvin; Shatkay, Hagit; Abraham, M Roselle
• Is Part Of: Journal of the American Medical Informatics Association. Volume 29:Number 11(2022)
• Page Start: 1879
• Page End: 1889
• Publication Date: 2022-08-03
• Subjects: hypertrophic cardiomyopathy -- electrocardiogram (ECG) -- delineation -- segmentation -- abnormalities; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocac122
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: Abnormalities in impulse propagation and cardiac repolarization are frequent in hypertrophic cardiomyopathy (HCM), leading to abnormalities in 12-lead electrocardiograms (ECGs). Computational ECG analysis can identify electrophysiological and structural remodeling and predict arrhythmias. This requires accurate ECG segmentation. It is unknown whether current segmentation methods developed using datasets containing annotations for mostly normal heartbeats perform well in HCM. Here, we present a segmentation method to effectively identify ECG waves across 12-lead HCM ECGs. Methods: We develop (1) a web-based tool that permits manual annotations of P, P′, QRS, R′, S′, T, T′, U, J, epsilon waves, QRS complex slurring, and atrial fibrillation by 3 experts and (2) an easy-to-implement segmentation method that effectively identifies ECG waves in normal and abnormal heartbeats. Our method was tested on 131 12-lead HCM ECGs and 2 public ECG sets to evaluate its performance in non-HCM ECGs. Results: Over the HCM dataset, our method obtained a sensitivity of 99.2% and 98.1% and a positive predictive value of 92% and 95.3% when detecting QRS complex and T-offset, respectively, significantly outperforming a state-of-the-art segmentation method previously employed for HCM analysis. Over public ECG sets, it significantly outperformed 3 state-of-the-art methods when detecting P-onset and peak, T-offset, and QRS-onset and peak regarding the positive predictive value and segmentation error. It performed at a level similar to other methods in other tasks. Conclusion: Our method accurately identified ECG waves in the HCM dataset, outperforming a state-of-the-art method, and demonstrated similar good performance as other methods in normal/non-HCM ECG sets.