A novel algorithm for ECG parametrization and synthesis
Parametrization and modeling of electrocardiogram (ECG) recordings are some of the most challenging areas of biomedical signal processing owing to the fact that ECG signals commonly exhibit complex temporal morphology and contain various artifacts of data collection process. In this paper, we propose a novel and fully automatic framework for highly accurate and robust ECG parametrization and reconstruction. The proposed method facilitates adaptive ECG signal modeling, and as such it is not constrained to an opportune combination of mathematical functions. The method relies on Dynamic Time warping (DTW) algorithm to establish the temporal relationship between the ECG model and the analyzed ECG pulses and to obtain their parametric description. Performance evaluation experiments conducted on a database of 40 one-minute ECG signal recordings, including examples of Normal Sinus Rhythm R Interval ECG record, Arrhythmia, Supraventricular Arrhythmia and Atrial Fibrillation, have shown that the proposed method is able to consistently produce accurate signal parametrization and reconstruction.