A deep learning model based on combining surface and esophageal ECG data for diagnosis of paroxysmal supraventricular...

This study aims to develop a deep learning model utilizing both surface and esophageal electrocardiogram (ECG) data to accurately differentiate types of paroxysmal supraventricular tachycardia (PSVT), including slow-fast atrioventricular nodal re-entrant tachycardia (S-F AVNRT), and orthodromic atrioventricular reentrant tachycardia with left-sided (AVRT-L) and right-sided (AVRT-R) accessory pathways.

We analyzed 921 ECG cases from 775 patients from four hospitals between 2014 and 2022, segmented into 6261 ten-second ECG segments. A Residual Network (ResNet)-based model was developed. For comparison, Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) classifiers were also constructed using handcrafted time-domain and frequency-domain features. It was thoroughly evaluated using a comprehensive set of metrics. These metrics included accuracy (ACC), the area under the receiver operating characteristic curve (AUC); sensitivity (SEN), specificity (SPE), positive predictive value (PPV), negative predictive value (NPV), and F1-Score.

The diagnostic efficacy of ECG lead configurations was robust. Among surface-only leads, the three-lead combination II+V₁+aVF achieved the highest AUC (0.989). The single-lead aVF demonstrated remarkable efficiency (AUC 0.961), approaching the performance of the full 12-lead ECG (0.974). The bipolar esophageal lead (EB) alone achieved an AUC of 0.989, comparable to II+V₁+aVF (AUC 0.989). The combination aVF+EB yielded the highest overall AUC of 0.996. ResNet significantly outperformed RF and XGBoost across all lead configurations (P < 0.001).

This model effectively distinguishes between PSVT types, surpassing traditional diagnostics in accuracy and reliability. Future research should focus on model optimization and dataset expansion to enhance diagnostic capabilities and interpretability.