Digit Health. 2024;10:20552076241281200.

Classification of underlying paroxysmal supraventricular tachycardia types using deep learning of sinus rhythm electrocardiograms.

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제1저자 권순일 · 교신저자 서장원, 최의근

PMID 39372813 PMC11450910 DOI 10.1177/20552076241281200 IF 3.9 순환기내과

소속 (6)

Division of Cardiology, Department of Internal Medicine, SMG-SNU Boramae Medical Center, Seoul, Republic of Korea.
Department of Intelligence and Information, Seoul National University, Seoul, Republic of Korea.
Division of Cardiology, Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea.
Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea.
Division of Cardiology, Department of Internal Medicine, Presbyterian Medical Center, Jeonju, Republic of Korea.
Interdisciplinary Program in Artificial Intelligence, Seoul National University, Seoul, Republic of Korea.

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 < 0.001). No significant difference was found in AUROC between 12-lead and precordial 6-lead ECGs (p = 0.265). On the test set, deep learning achieved modest performance in differentiating the two types of arrhythmias, with an AUROC of 0.708, an AUPRC of 0.875, an F1-score of 0.750, a sensitivity of 0.670, and a specificity of 0.649.

CONCLUSION The deep-learning classification of AVNRT and concealed AVRT using sinus rhythm ECGs is feasible, indicating potential for aiding in the non-invasive diagnosis of these arrhythmias.

Keywords

Atrioventricular nodal reentry tachycardiaatrioventricular reentry tachycardiadeep learningparoxysmal supraventricular tachycardia

Figures

**Figure 1.** Overview of the research design for classifying AVNRT and concealed AVRT using sinus rhythm ECGs. The deep neural network is pre-trained on the PhysioNet/CinC 2021 datasets and further trained to differentiate between sinus rhythm ECGs with underlying AVNRT and concealed AVRT. After the model selection process using ten-fold cross-validation on the training set, the performance of the final model on the test set is reported.

**Figure 2.** ROC curves and C-statistics (AUROCs) for selected ECG features. AUROC: area under the receiver operating characteristic curve; ECG: electrocardiogram.

**Figure 3.** Performance of deep learning in ten-fold cross-validation for PSVT classification. (Upper row) Comparison of the model with and without pre-training, including (a) receiver operating curves (AUROCs) and (b) precision-recall curves (AUPRCs). (Lower row) Comparison of the model using different sets of leads, including (c) receiver operating curves (AUROCs) and (d) precision-recall curves (AUPRCs). AUPRC: area under the precision-recall curve; AUROC: area under the receiver operating characteristic curve; ECG: electrocardiogram.

**Figure 4.** Visualization of feature attribution on ECG segments for PSVT classification. AVNRT: atrioventricular nodal reentry tachycardia; AVRT: atrioventricular reentry tachycardia; ECG: electrocardiogram; PSVT: paroxysmal supraventricular tachycardia.

**Figure 5.** Performance of the deep learning model with varying training dataset sizes. AUROC: area under the receiver operating characteristic curve.

그림 출처: PubMed Central 공개 논문 (PMC11450910)

Conflict of interest statement

The authors declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: S. Kwon, J. Suh, J. Kim, H. Ju, H.-J. Ahn, S. Kim, S.-R. Lee, and S. Oh: None to disclose. W. Rhee: received research grants from Samsung Electronics, NAVER, SK Hynix, and SK Telecom outside the submitted work. E.-K. Choi: received grants from Bayer, BMS/Pfizer, Biosense Webster, Chong Kun Dang, Daewoong Pharmaceutical Co, Daiichi-Sankyo, DeepQure, Dreamtech Co., EIL Pharmaceutical Co., Medtronic, Sanofi-Aventis, Samjinpharm, Seers Technology, and Skylabs outside the submitted work.

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