International Journal of Computer Applications (0975 – 8887) Volume 99– No.2, August 2014 34 Denoising Baseline Wander Noise from Electrocardiogram Signal using Fast ICA with Multiple Adjustments Nevi Jain Department of Electronics and Comm. Engg. Samrat Ashok Technological Institute Vidisha, M.P., India Devendra Kumar Shakya Department of Bio-Medical Engg. Samrat Ashok Technological Institute Vidisha, M.P., India ABSTRACT The electrocardiogram (ECG) is widely utilitarian for prognostic of heart diseases. Quality and utilization of ECG signal is affected by different noises and hence it is very difficult to measure important parameter to know the exact condition of heart. Baseline wander is one type of noise which is normally seen in ECG signal. This artifact severally limits the usefulness of recorded ECG signals and thus need to be removed for better clinical appraisal. Independent component analysis (ICA) is a statistical technique for estimating a multidimensional random vector into components that are statistically not dependent from each other. This paper proposed the implementation of fast ICA with multiple adjustments for removing baseline wander noise effect from ECG. Simulation results demonstrate that the proposed method is better in denoised the baseline wander noise from ECG signal. General Terms ECG, ECG Denoising, Baseline Wader Noise, Independent Component Analysis Keywords Electrocardiogram, Baseline Wander Noise, Variable Notch Filter, Independent Component Analysis. 1. INTRODUCTION The electrocardiogram (ECG) is the recording of the electrical activity of heart which state the condition of heart is extensively utilitarian for diagnostic of heart diseases. Advising of ECG is a non-invasive technique which generally used as a preparatory diagnostic tool for cardiovascular diseases. Due to feeble non-stationary nature of ECG signal easily interfere by noise. To obtain noise free ECG signal denoising is the procedure to aloof the licit signal component from undesired signals [1]. A noise free electrocardiogram signal gives information about the electrophysiology of the heart diseases and ischemic changes that may occur [2]. Generally ECG signals frequency range is 0.05-100Hz and dynamic range of ECG signal is 1-10mv. Basically ECG signal is characterized by five valleys and peaks points by the P, Q, R, S, and T. and its waveform is repetitive and have various bumps and parts of the waveform are designated as the P-wave, QRS-complex and T-wave, PR-segment, ST- segment, PR-interval and QT-interval as given in Figure 1. Baseline wander noise is the low frequency activity in ECG signal. Due to this noise measurement of ECG parameters produce correct information is a tedious job. It can be induced by electrode changes due to perspiration, movement and respiration. Removing the baseline drift in ECG signal is most essential, if it not properly removed than some important information will be corrupted or lost. The frequency range of baseline wander noise is generally below 0.5 Hz which is identical as the frequency range of ST-segment [3]-[4]. Different techniques used for estimating removal baseline wander. The high pass filter used with the 0.5 Hz cut –off frequency. This cut-off frequency is essential for removing baseline wander and it should be preferred so that the clinical information in ECG signals remains not distorted [5]. Digital filters are generally employed to removal baseline wander noise. The cut-off frequency and phase response are two most important factors considered in digital filter designs. The utilization of linear phase filters prevents the issue of phase distortion and estimating the baseline wander [6]. Finite impulse response (FIR) filters having feed forward elements and it’s usually implemented using non-recursive structures. It can have an exact linear phase. Infinite impulse response (IIR) Fig 1: The ECG signal filters are having feedback elements and IIR filters are using recursive structure. Infinite impulse response (IIR) zero phase filtering also employed removing of baseline wander [7]-[8]. These methods are use cut-off frequency for removing baseline wander and provide undistorted ECG signals. Cubic spline curve fitting and linear spline curve fitting are the different filtering techniques which remove baseline wander by taking reference points. Cubic spline is defined as the isoelectric references points are compulsory for proper