Introduction: Fragmented QRS complexes (fQRS) are QRS complexes with one or more deflections. They are known risk factors for cardiac events in several patient groups. Detection is done visually, which is a time-consuming process that may lead to subjective results, limiting the clinical use of this parameter.
Methods: We propose an automated method to calculate an fQRS score which gives an indication of the severity of fQRS in a channel. To compute the score, 10 features are calculated using Phase Rectified Signal Averaging and Variational Mode Decomposition and used as input for an SVM classifi-er. The fQRS score is then used to assess the risk of all-cause mortality in a da-taset of patients with an implanted cardioverter defibrillator. An optimal cut point is defined for each channel to dichotomize the fQRS scores. Boot-strapping is used to reduce variability in cut point selection.
Results: Classification results (AUC=0.926) show that the fQRS score suc-ceeds in separating signals with clear QRS fragmentation from normal sig-nals. Results of survival analysis on an independent test set indicate that the fQRS score of 3 channels leads to survival curves with statistically significant differences (p<0.05). The channels which show the most obvious results are in line with results in previous clinical studies.
Conclusion: This novel way of detecting and quantifying QRS fragmentation is therefore a promising way to promote the clinical usefulness of the param-eter.