Waveform Compensation of ECG Data Using Segment Fitting Functions for Individual Identification
Document Type
Conference Proceeding
Publication Date
2017
DOI
10.1109/CIS.2017.00110
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.
Source Publication
2017 13th International Conference on Computational Intelligence and Security (CIS)
ISBN
978-1-5386-4822-3
First Page
475
Last Page
479
Recommended Citation
He, C.,Li, W.,& CHIK, T. (2017). Waveform Compensation of ECG Data Using Segment Fitting Functions for Individual Identification. 2017 13th International Conference on Computational Intelligence and Security (CIS), 475-479. http://dx.doi.org/10.1109/CIS.2017.00110