Artificial intelligence–enabled electrocardiogram to distinguish atrioventricular re-entrant tachycardia from atrioventricular nodal re-entrant tachycardia

• Published in: Cardiovascular digital health journal Vol. 4; no. 2; pp. 60 - 67
• Main Authors: Sau, Arunashis, Ibrahim, Safi, Kramer, Daniel B., Waks, Jonathan W., Qureshi, Norman, Koa-Wing, Michael, Keene, Daniel, Malcolme-Lawes, Louisa, Lefroy, David C., Linton, Nicholas W.F., Lim, Phang Boon, Varnava, Amanda, Whinnett, Zachary I., Kanagaratnam, Prapa, Mandic, Danilo, Peters, Nicholas S., Ng, Fu Siong
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.04.2023; Elsevier
• Subjects: Ablation; Artificial intelligence; Atrioventricular nodal re-entrant tachycardia; Atrioventricular re-entrant tachycardia; Electrocardiogram; Electrophysiology study; Machine learning; Original; Atrioventricular nodal re-entrant tachycardia; Electrocardiogram; Machine learning; Atrioventricular re-entrant tachycardia; Artificial intelligence; Ablation; Electrophysiology study
• ISSN: 2666-6936; 2666-6936
• DOI: 10.1016/j.cvdhj.2023.01.004

Accurately determining arrhythmia mechanism from a 12-lead electrocardiogram (ECG) of supraventricular tachycardia can be challenging. We hypothesized a convolutional neural network (CNN) can be trained to classify atrioventricular re-entrant tachycardia (AVRT) vs atrioventricular nodal re-entrant tachycardia (AVNRT) from the 12-lead ECG, when using findings from the invasive electrophysiology (EP) study as the gold standard. We trained a CNN on data from 124 patients undergoing EP studies with a final diagnosis of AVRT or AVNRT. A total of 4962 5-second 12-lead ECG segments were used for training. Each case was labeled AVRT or AVNRT based on the findings of the EP study. The model performance was evaluated against a hold-out test set of 31 patients and compared to an existing manual algorithm. The model had an accuracy of 77.4% in distinguishing between AVRT and AVNRT. The area under the receiver operating characteristic curve was 0.80. In comparison, the existing manual algorithm achieved an accuracy of 67.7% on the same test set. Saliency mapping demonstrated the network used the expected sections of the ECGs for diagnoses; these were the QRS complexes that may contain retrograde P waves. We describe the first neural network trained to differentiate AVRT from AVNRT. Accurate diagnosis of arrhythmia mechanism from a 12-lead ECG could aid preprocedural counseling, consent, and procedure planning. The current accuracy from our neural network is modest but may be improved with a larger training dataset.