Fragmented QRS (fQRS) is an electrocardiographic (ECG) marker of myocardial conduction abnormality, characterized by additional notches in the QRS complex. The presence of fQRS has been associated with an increased risk of all-cause mortality and arrhythmia in patients with cardiovascular disease. However, current binary visual analysis is prone to intra- and inter-observer variability and different definitions are problematic in clinical practice. Therefore, objective quantification of fQRS is needed and could further improve risk stratification of these patients. We present an automated method for fQRS detection and quantification. First, a novel robust QRS complex segmentation strategy is proposed, which combines multi-lead information and excludes abnormal heartbeats automatically. Afterwards extracted features, based on variational mode decomposition (VMD), phase-rectified signal averaging (PRSA) and the number of baseline-crossings of the ECG, were used to train a machine learning classifier (Support Vector Machine) to discriminate fragmented from non-fragmented ECG-traces using multi-center data and combining different fQRS criteria used in clinical settings. The best model was trained on the combination of two independent previously annotated datasets and, compared to these visual fQRS annotations, achieved Kappa scores of 0.68 and 0.44, respectively. We also show that the algorithm might be used in both regular sinus rhythm and irregular beats during atrial fibrillation. These results demonstrate that the proposed approach could be relevant for clinical practice by objectively assessing and quantifying fQRS. The study sets the path for further clinical application of the developed automated fQRS algorithm.

Department of Cardiac Sciences Libin Cardiovascular Institute Cumming School of Medicine University of Calgary Calgary Alberta Canada

Department of Cardiology and Pneumology Heart Center University of Göttingen Medical Center Göttingen Germany

Department of Cardiology University Medical Center Utrecht Utrecht Netherlands

Department of Cardiovascular Diseases Experimental Cardiology KU Leuven Leuven Belgium

Department of Electrical Engineering STADIUS Center for Dynamical Systems Signal Processing and Data Analytics KU Leuven Leuven Belgium

Department of Internal Medicine and Cardiology Masaryk University Brno Czech Republic

Department of Medical Statistics University Medical Center Göttingen Göttingen Germany

Division of Cardiology University of Basel Hospital Basel Switzerland

DZHK partner site Göttingen Göttingen Germany

Microgravity Research Center Université Libre de Bruxelles Brussels Belgium

National Heart and Lung Institute Imperial College London UK

Research Unit of Internal Medicine Medical Research Center Oulu University of Oulu and Oulu University Hospital Oulu Finland

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