Waveform Compensation of ECG Data Using Segment Fitting Functions for Individual Identification

Abstract

Physiological signals can be considered as a source of biometric characteristics that allow biometric identification. The aim of this research is to assess the effect of fitting methods on the morphological features of electrocardiogram (ECG) signals. Three different families of fitting functions have been selected to verify the performance of curve fitting. The experiment result shows that the fitting methods would be efficient for individual identification by ECG classification based on these fitting parameters.