International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 10 Issue: 04 | Apr 2023
p-ISSN: 2395-0072
www.irjet.net
ECG SIGNAL DE-NOISING USING DIGITAL FILTER TECHNIQUES Dr. V.V.K.D.V. Prasad1, B. Nagasirisha2, K. Baby Amulya3, K. Sarada4, G. Naga Sirisha5 1Dr. V.V.K.D.V. Prasad, Professor, Department of Electronics and communication Engineering, Seshadri Rao
Gudlavalleru Engineering College, Andhra Pradesh, India
2B. Nagasirisha Assistant Professor, Department of Electronics and communication Engineering, Seshadri Rao
Gudlavalleru Engineering College, Andhra Pradesh, India ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - The function of the heart is analysed by the
Electrocardiogram(ECG) signal. But the ECG electrodes recorded ECG signal cannot be used directly for further processing. Several kinds of noises may corrupt the ECG signal in recording process. Usually the ECG signals are contaminated by the baseline wander noise (BLW), muscle noises (EMG) and moving electrode artifact (MA). Therefore, to enhance the ECG signal appropriate digital filtering techniques are used. In this study, discrete wavelet transforms (DWT) and low pass filter (LPF) methods are used to de-noise the ECG signal. Further moving mean method, linear regression method and savitzky-golay smoothing techniques are applied for ECG signal enhancement. Mean square error (MSE) and signal to noise ratio (SNR) parameters are evaluated to assess the noise removing capability of the methods. From the results it is observed that the LPF with moving mean smoothing method show superior performance in ECG De-noising.
Fig-1: ECG signal The ECG signal cannot be directly altered or processed using conventional procedures when it has been tampered with different types of noise. In general, digital filters are the best choice for reducing noise in ECG signals. P.C. Bhaskara and M. D. Uplaneb suggested a low pass FIR filter with different windowing techniques that can cancels the high-frequency noise effectively from the ECG signals [7]. Excellent noise reduction services were offered by Kaiser Window. Chaudhary M. and Narwaria R. P. compared the performance of different digital filters IIR, Butterworth, Chebyshev Type-I and Type-II in terms of the signal to noise ratio and average power. Concerned work results demonstrated that a low pass Butterworth filter might reduce noise more effectively than the alternatives. Mahawar et al. developed a FIR low pass filter with strong attenuation using the windowing approach, Kaiser and Dolph-Chebyshev (DC), and DC. Based on some of the performance metrics Sharma M. and Dalal H. reported that FIR and IIR digital filters can remove the lowfrequency baseline drift from the ECG signal [4]. In order to de-noise the Baseline Wander interferences, Rastogi, N. and Mehra, R. developed an integrated strategy that combines Daubechies wavelet decomposition with a variety of thresholding techniques and the IIR digital Chebyshev or Butterworth filter [4].
Key Words: ECG signal, BLW noise, EMG noise, moving artefact, DWT, low pass filter, moving mean, linear regression, savitzky-golay.
1.INTRODUCTION The ECG signal is shown in the figure 1. To understand the function of heart first has to analyses the PQRST wave. The P wave that indicates the polarization of the arteries, QRS complex that indicates the polarization of ventricles and depolarization of arteries, T wave that indicates the repolarizing of the ventricles. Usually the ECG signal is small at range from 0.1mV to 20mV and frequency range of 0.05 Hz to 100 Hz. Generally, the cardiac function is represented by the ECG signal. To record the ECG signal multiple ECG electrodes are placed on the skin in an Einthoven structure. The recorded ECG signal is corrupted by the different types of noise, such as BLW noise, motion artifact, EMG noise, power line interference noise (PLI), electrode noise and high frequency noise can taint the raw ECG signal. Among these noise BLW noise, EMG noise and MA noise are more pervasive in process. Past research stated that the advancement in ECG signal de-noising, feature extraction, event detection and classification play crucial role in medical diagnosis and clinical applications.
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Based on the earlier survey concluded that ECG signal preprocessing and smoothing techniques are essential in ECG signal enhancement. Since the first and foremost step commence here is the reduction of noise and next is smoothing the de-noised ECG signal [2] using different digital filtering techniques.
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