1. Classification of underlying paroxysmal supraventricular tachycardia types using deep learning of sinus rhythm electrocardiograms
- Author
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Soonil Kwon, Jangwon Suh, Eue-Keun Choi, Jimyeong Kim, Hojin Ju, Hyo-Jeong Ahn, Sunhwa Kim, So-Ryoung Lee, Seil Oh, and Wonjong Rhee
- Subjects
Computer applications to medicine. Medical informatics ,R858-859.7 - Abstract
Background Obtaining tachycardia electrocardiograms (ECGs) in patients with paroxysmal supraventricular tachycardia (PSVT) is often challenging. Sinus rhythm ECGs are of limited predictive value for PSVT types in patients without preexcitation. This study aimed to explore the classification of atrioventricular nodal reentry tachycardia (AVNRT) and concealed atrioventricular reentry tachycardia (AVRT) using sinus rhythm ECGs through deep learning. Methods This retrospective study included patients diagnosed with either AVNRT or concealed AVRT, validated through electrophysiological studies. A modified ResNet-34 deep learning model, pre-trained on a public ECG database, was employed to classify sinus rhythm ECGs with underlying AVNRT or concealed AVRT. Various configurations were compared using ten-fold cross-validation on the training set, and the best-performing configuration was tested on the hold-out test set. Results The study analyzed 833 patients with AVNRT and 346 with concealed AVRT. Among ECG features, the corrected QT intervals exhibited the highest area under the receiver operating characteristic curve (AUROC) of 0.602. The performance of the deep learning model significantly improved after pre-training, showing an AUROC of 0.726 compared to 0.668 without pre-training ( p
- Published
- 2024
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