Performance Analysis of different filters for power line interface reduction in ECG signal

Abstract

Over the years Computer aided analysis of ECG signal is gaining with tremendous amount of work being carried out all over the world. This paper is a small step on our part in that direction, ECG Electrocardiogram signal most comely known recognized and used biomedical signal, the ECG signal is very sensitive in nature, and even if small noise mixed with original signal the various characteristics of the signal changes, Data corrupted with noise must either filtered or discarded, filtering is important issue for design consideration of real time heart monitoring systems. The purpose of this paper is to quantify relative performance analysis of different filtering methods for power line interface reduction.The data base for the performance analysis is created by simulation of ECG signal , an ideal ECG signal is best for performance analysis, then data base is corrupted with 50 Hz power line interface ,the ability of different filter (use IIR Notch , Wiener, adaptive filter) are checked by changes in filtered signal, signal to noise ratio, Power of the signal, Power spectral density ,spectrogram of the signal , The location of peaks and its amplitude also measured by Pan Tompkins algorithm for performance analysis of filters. The results have clearly indicated that there is reduction in Power line noise in the ECG signal changes according to filter, and the best result is shown by adaptive filter we can see it easily in spectrogram, The results have been concluded using Mat lab and Simulated ECG database.

An integration of improved median and morphological filtering techniques for electrocardiogram signal processing

“…Impulse noise is a common artifact which affects the peaks of the ECG signal. Another category of distortion is baseline wander, which causes the baseline (of ECG waveform) to drift up and down in a sinusoidal pattern due to respiration [4]. Therefore, ECG signal conditioning for providing baseline correction and noise elimination is a necessary requirement for further analysis such as QRS detection and temporal alignment.…”

ECG signal denoising by using least-mean-square and normalised-least-mean-square algorithm based adaptive filter

“…So, the removal of these noise and interference from the ECG signal has become very crucial. Different types of digital filters (FIR and IIR) have been used to solve the problem [3][4][5]. However, it is difficult to apply these filters with fixed coefficients to reduce different types of noises, because the ECG signal is known as a non-stationary signal.…”

Cardiac arrhythmia detection in ECG signals by feature Extraction and support vector machine

“…So the proper attention required correcting the baseline wander inference, to remove the low frequency noise and baseline wander we use a low pass Butterworth filter. Butterworth filter also best for removal of high frequency interferences due to its simplicity and maximum flat magnitude response, the filter function is represented as Classification of arrhythmia by support vector machine requires generation of input vector; an input vector to classification should include the morphology and rhythm feature of ECG signal as shown in figure [3]. Feature extraction step is very influential for arrhythmia detection.…”

“…Adaptive filter is a kind of filter in which changing of filter parameter give optimum results for power line interference reduction [3], Adaptive filters 'learn' with initial input signal for track them to remove the signal interferences by changing the filter parameters.…”