A Lead-Specific Deep Learning Framework for QRS Notch Detection in 12-Lead Electrocardiograms
Year: 2026
Type: article
Abstract: 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.
Source: BioMedInformatics
Institutions: Kochi University of Technology, Kanazawa Medical University
Language: English
Topic: ECG Monitoring and Analysis
Subfield: Cardiology and Cardiovascular Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,207
OpenAlex ID: W7225663831
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