An Efficient Piecewise Modeling of ECG Signals Based on Hermitian Basis Functions

In this paper, a new piecewise modeling for approximation of ECG signal is presented. Most of the modeling methods are focused to obtain the best approximation of the entire ECG signal. The proposed method exploits the importance of different intervals of ECG signals, in particular QRS complex, by performing a segmented based modeling using Hermitian basis functions. This yields to weighting the approximation error of each segment based on its importance throughout the ECG complex. As the result shows the total error obtained in this method is almost halved in comparison with similar non-segmented method. This has a great impact in modeling the heart arrhythmias where a small error could mislead the diagnosis. The presented method uses only the 5^th order Hermitian basis functions which considerably reduce the total parameters needed to represent the ECG signal in comparison with other Hermitian based methods.