A Lead-Specific Deep Learning Framework for QRS Notch Detection in 12-Lead Electrocardiograms

Background: Automated QRS notch detection is challenging because QRS amplitude, morphology, and notch prevalence vary substantially across the 12 ECG leads. This study aimed to develop a lead-specific deep learning framework that explicitly accounts for these lead-dependent differences. Methods: The framework combined lead-targeted modeling with anatomically related context leads, lead-specific parameter optimization, and lead-matched architectures. Eleven leads were modeled using the target lead and related context leads, whereas aVR was modeled independently because of its distinct reciprocal orientation. The framework was evaluated using 21,799 PTB-XL recordings with PTB-XL+ notch annotations and patient-wise five-fold cross-validation. Results: The proposed framework achieved a mean lead-level F1-score of 84.20%, improving performance by approximately 13.29 percentage points over the best pooled baseline across the 12 leads. Ablation analyses showed improvements from anatomical context input (+4.68 percentage points) and lead-specific parameter optimization (+4.77 percentage points), while the lead-matched architectures also consistently outperformed the baseline architectures. Conclusions: These findings indicate that QRS notch detection benefits from explicitly modeling lead-level anatomical and statistical differences rather than applying a uniform model across all ECG leads.