Atrial repolarization wave: a new approach using spline-based feature engineering and explainable AI for atrial arrhythmia diagnosis

OBJECTIVES: The atrial repolarization (Ta wave) characteristics remains largely unexplored, given its inherently low amplitude and obscured by the QRS complex. Hence, this study aims to witness Ta wave within QRS complex. METHODS: 10 s ECGs of 50 Sinus Rhythm (SR), 50 Sinus Tachycardia (SiT) and 20 Atrial Tachycardia (AT) were recorded using standard 12-lead. The datapoints were extracted from pre-processed Lead-II and three spline model interpolated hidden Ta wave post fiducial point detection. Further, validation analysis was performed with and without QRS complex to select the optimal spline model with the Ta wave of SiT Modified Limb Lead (MLL) & Atrio-Ventricular block (AVB) ECG. RESULTS: It was noted that the cubic spline interpolation model gave the best SSIM score of 0.85 and lowest power spectrum % difference of 0.1 % for Ta wave interpolation without QRS complex. Further, ECG-based Ta temporal and voltage features were crafted. Statistically significant features were used for five ML models multi-class classification. Extra-Trees model gave the best output with 99 % using P-Ta feature combined. CONCLUSIONS: Overall, the proposed method demonstrated that along with the existing P wave features, Ta wave features have potential in better classification of atrial arrhythmia, while interpolation model offers ease of implementation and adaptability to diverse clinical applications.