Application Of Polynomial Approximation Techniques For Smoothing Ecg Signals

Bioelectric signals are generated by nerve cells and muscle cells. The sources of these signals are cells which undergo change of state from resting potential to action potential under certain conditions [1]. The bio-electric signals generated from heart muscles are known as ECG signals. ECG is the graphical representation of electrical activity of heart and widely used for diagnosis of various heart diseases. The ECG signal is recorded with the help of surface electrodes. While recording, different artifacts get introduced in the signal like Electrode contact noise, motion artifacts, base line drift, base line wander, electrosurgical noise, and power line interferences [2,3]. In order to extract accurate and meaningful clinical information of heart from ECG these artifacts have to be discarded or filtered out for which analog and digital signal processing is required. Due to less hardware requirement, digital signal processing is now used widely, offering more advantages over the analog one [4 -6]. Polynomials are frequently used in signal processing; e.g., for filtering noisy signals, for interpolation of data, and for data compression. All these applications use low-order By