Quantification of fragmented QRS complex using intrinsic time-scale decomposition. (January 2017) - Jin, Feng; Sugavaneswaran, Lakshmi; Krishnan, Sridhar; Chauhan, Vijay S.
Abstract : Highlights: This study proposes an automated method to quantify QRS fractionation using ITD. Instantaneous features are extracted for characterization of fragmented QRS complex. A novel index that quantifies the variations in fQRS shapes is introduced. The potential of the novel metric is validated for the PTB real-world ECG database. ROC analysis showed an area under the curve of 0.96 when fQRS was quantified. Further investigation can facilitate SCD risk assessment in patients. Abstract: The QRS complex recorded from the surface electrocardiogram (ECG) arises from electrical activation of the ventricular myocardium through the normal conduction system. The presence of a fragmented QRS (fQRS) complex reflects abnormal electrical activation and has been recently shown to identify patients with heart disease at risk of sudden cardiac death (SCD). The evaluation of fQRS is currently performed qualitatively by visual inspection which can be time consuming and subject to interpretation. Moreover, qualitative assessment of QRS for fragmentation may be insensitive to more subtle deflections in the QRS complex that may be equally prognostic. This study proposes an automated method to quantify QRS fractionation using intrinsic time-scale decomposition (ITD). Instantaneous morphology features are extracted from the decomposed QRS signal to index variations in its shapes. Our quantitative fQRS metric was found to be significantly greater in QRS complexes with fragmentation compared to normal QRS complexes derived from real-world ECGs in the Physikalisch-Technische Bundesanstalt (PTB) database. ROC analysis showed an area under the curve of 0.96 when fQRS was quantified from the precordial ECG leads, V1–V6. Thus, quantification of fQRS using the proposed ITD-based method can accurately identify fQRS. Our approach shows tremendous potential and could be investigated further for SCD risk assessment in patients with heart disease by improving the identification of fQRS that may or may not be visually apparent.