Fractional Order Filtering Approach Towards ECG Non Stationary Biomedical Signal Processing

Abstract

Due to the increase of desk-bound activities and sedentary lifestyle, cardiac diseases are increasing at an alarming rate especially in developing countries like India. According to the WHO data, one in four Indians died, because of cardiovascular diseases. Electrocardiogram (ECG) is the signal originated due to human heart activities. It is a record of the electrical commotion caused by depolarization and repolarization of the atria and ventricles of the heart muscles. ECGs are used to find anomalies in the heart beat which may be indicative of various cardiovascular diseases. Accurately detecting the anomalies in an ECG is the relevant issue of the medical field. Each beat of an ECG is composed of several pulses of different bandwidths (known as waves P, Q, R, S and T), and an iso-electric period which corresponds to the lapse of time between two consecutive beats. As the behaviour of an ECG waveform changes with time, so it is non-stationary, pseudo periodic in nature. ECG is a powerful tool in determining the health and functioning of the heart. Faster detection and diagnosis of the cardiovascular conditions would aid physicians to provide appropriate treatment to the patients. Proper processing of an ECG signal and its accurate detection is very much essential as it determines the condition of the heart. The analysis of an ECG signal requires the information both in time and frequency, for clinical diagnosis. The non-stationarity of an ECG is often corrupted by low and high frequency noise components like power line interference (PLI), baseline wander, electromyogram, motion artifacts, etc. These different artifacts affect the morphology of the ECG waveform, thus making its analysis a difficult job. There are many digital filtering techniques which are used in its processing in order to perform this task on computer-aided diagnosis. Finite Impulse Response (FIR) has been extensively used for ECG filtering. However, there seems to be an improvement for designing filter based upon self-convolution window