Erol Dogan, GizemnurTezel, GulaySolak, Fatma ZehraUzbas, Betul2025-12-242025-12-2420251863-17031863-1711https://doi.org/10.1007/s11760-025-04960-5https://hdl.handle.net/123456789/1273612-Lead Electrocardiography (ECG) is an essential diagnostic tool for detecting Cardiac Arrhythmias (CAs). In this study, Arrhythmia Detection (AD) was conducted using a 12-Lead ECG dataset. The dataset underwent specific preprocessing, and a hybrid 2-Phase Feature Extraction (2-PFE) method was proposed: (1) QRS detection using the Pan-Tompkins algorithm, and (2) P-Peak detection using windowing. The study specifically focused on analyzing Atrial Fibrillation (AFIB) separately from other arrhythmia types. This approach was evaluated through three classification models: SR and NON-SR, SR and NON-SR without AFIB, SR and AFIB.eninfo:eu-repo/semantics/closedAccess12-Lead ECGArrhythmia DetectionAtrial FibrillationFeature ExtractionMachine LearningArticle10.1007/s11760-025-04960-52-s2.0-105021967635