An Extra-Trees machine learning model using combined P and Ta wave features achieved 99% accuracy for multi-class classification of atrial arrhythmias.
Does the inclusion of spline-interpolated Ta wave features improve machine learning classification of atrial arrhythmias?
Extracting hidden Ta wave features using cubic spline interpolation significantly enhances machine learning classification of atrial arrhythmias.
Abstract 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.
Bhardwaj et al. (Sat,) conducted a other in Atrial arrhythmia (n=120). Spline-based feature engineering and Extra-Trees machine learning model was evaluated on Multi-class classification of atrial arrhythmias. An Extra-Trees machine learning model using combined P and Ta wave features achieved 99% accuracy for multi-class classification of atrial arrhythmias.