Multidimensional Approaches for Noise Cancellation of ECG signal Akash Kumar Bhoi, Karma Sonam Sherpa, Devakishore Phurailatpam, Jitendra Singh Tamang, Pankaj Kumar Giri Abstract: In many situations, the Electrocardiogram (ECG) is recorded during ambulatory or strenuous conditions such that the signal is corrupted by different types of noise, sometimes originating from another physiological process of the body. Hence, noise removal is an important aspect of signal processing. Here five different filters i.e. median, Low Pass Butter worth, FIR, Weighted Moving Average and Stationary Wavelet Transform (SWT) with their filtering effect on noisy ECG are presented. Comparative analyses among these filtering techniques are described and statically results are evaluated. Index Terms- Electrocardiography, Active noise reduction, Filters, Noise cancellation. I. INTRODUCTION A physician can detect a heart problem from this information and can suggest timely measures. But during the acquisition of ECG signal, it may get corrupted by different types of noises [21] which make it difficult for the physician to give his diagnosis. Power Line Interference (PLI) is one such kind of noise which superimposes on the vital information. The frequency range of ECG signal is 0.05Hz to 150Hz, and the frequency of the PLI noise is 50/60 Hz which lies within the frequency spectrum of the ECG signal, so for the meaningful and accurate detection, steps have to be taken to filter out or discard all these noise sources. Hamilton PS in his article compared adaptive and non adaptive filters for reduction of power line interference in the ECG [5]. Iders YZ, Saki MC, Gcer HA have developed a method for line interference reduction to be used in signal averaged electrocardiography [6]. Cramer E, McManus CD, Neubert D has introduced a global filtering approach. In this method two types of the digital filters are used where, one is using lest square method and other is using special summation method [7]. Different scientists have tried for removing the power line interference and base line wonder specifically from the ECG signal [8-17]. Zschorlich VR and Zschorlich VR, have also designed digital filters to cope with EMG signals [18-19]. Webster has explained the instrumentation requirements for the ECG [20]. Akash Kumar Bhoi is with the Applied Electronics & Instrumentation Engineering Department, Sikkim Manipal Institute of Technology (SMIT), India (email: akash730@gmail.com). Karma Sonam Sherpa is with the Electrical & Electronics Engineering Department, Sikkim Manipal Institute of Technology (SMIT), India (email: karmasherpa23@gmail.com). The wavelet coefficients represent a measure of similarity in the frequency content between a signal and a chosen wavelet function [1]. These coefficients are computed as a convolution of the signal and the scaled wavelet function, which can be interpreted as a dilated band-pass filter because of its band-pass like spectrum [2]. Sander et al. designed a 50/60Hz notch filter to eliminate baseline drift from high resolution ECG Signal [3]. Markovsky et al. used band pass, kalman adaptive filter for removal of resuscitation artifacts from human ECG signal [4]. For wavelet transform, daubechies wavelets were used because the scaling functions of this wavelet filter are similar to the shape of the ECG. From the decomposition of the ECG signal it was seen that the low frequency component cause the baseline shift, theses component were deducted to get a signal without baseline drift. Also the high frequency components of the signal were removed for getting denoised signal [22]. Below chapters sequentially elaborate the filtering performances of five different filters. II. METHODOLOGY Fig.1. Block diagram of proposed methodology “Fig. 1” illustrates the workflow of proposed methodology. Filtering operations are performed for the selected initial waveform (i.e. 4 sec data) “fig.2” of the full length noisy ECG signal for better visualization of filtering results “fig.3-7”. The statistical analysis results are shown in “fig.8-12”. Devakishore Phurailatpam is with the Electrical & Electronics Engineering Department, National Institute of Technology, Manipur, India (email: bungcha@gmail.com). Jitendra Singh Tamang is with Electronics & Communication Department, SMIT, India (email: js.tamang@gmail.com). Pankaj Kumar Giri is with and Applied Electronics & Instrumentation Engineering Department, SMIT, India (email: pankajdav09@gmail.com). ECG signal Noise Cancellation Filtering Efficiency Analysis International Conference on Communication and Signal Processing, April 2-4, 2015, India ISBN 978-1-4799-8080-2 Adhiparasakthi Engineering College, Melmaruvathur 060