ArticleDigital health
A deep learning model based on combining surface and esophageal ECG data for diagnosis of paroxysmal supraventricular tachycardia.
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10 authors.
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Abstract
Objective: 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. Methods: 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. Results: The diagnostic efficacy of ECG lead configurations was robust. Among surface-only leads, the three-lead combination II+V Conclusion: 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.
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