Elimination of Noise from Ambulatory ECG Signal using DWT

Elimination of Noise from Ambulatory ECG Signal using DWT

© 2022 by IJETT Journal
Volume-70 Issue-5
Year of Publication : 2022
Authors : Sonali Lohbare (Pakhmode) , Swati Dixit
DOI :  10.14445/22315381/IJETT-V70I5P229

How to Cite?

Sonali Lohbare (Pakhmode) , Swati Dixit, "Elimination of Noise from Ambulatory ECG Signal using DWT," International Journal of Engineering Trends and Technology, vol. 70, no. 5, pp. 266-273, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I5P229

The proposed work presents an efficient Wavelet Transform for noise removal from ambulatory cardiac signals and finds the R-R intervals making the signal ready for diagnosis. Noise affects cardiac analysis by creating some outliers. ECG signals can get corrupted by power line interference; direct current (DC) offset noise. These noises need to be filtered using moving average, wiener filter, etc. Some additional outlier bands might be attached to the ECG signal upon filtering. These signals can be removed using IIR (Infinite Impulse Response) and FIR (Finite Impulse Response) filters. Discrete Wavelet Transform (DWT) based technique designed using Python software is implemented in de-noising the Electrocardiograph (ECG) signal. The performance of de-noising is based on parameters like the selection of the wavelet (mother) function, the threshold method selected, the threshold value chosen, and the level of decomposition. Standard ECG signals available in the Physionet database, specifically the Arrhythmia Database MIT-BIH and PTB Database, are used to implement the proposed DWT method. Daubechies 8 (db8) is employed for high amplitude R wave detection. R waves are reference waves used in detecting the other waves by searching maxima and minima using windows. The same method also gives other features of R-R interval, QRS complex, P-R interval, ST deviation, and heart rate. SNR (Signal-to-Noise Ratio), MSE (Mean Square Error), RSME (Root Mean Square Error), and PSNR (Peak Signal-to-Noise Ratio) for soft and hard threshold techniques using Bayes Shrink and Visu Shrink methods were also estimated. The proposed approach gives maximum PSNR, average normalized MSE of 0.96, and SNR of 74%. The method proposed exhibits considerable noise elimination from ECG signals, and the de-noised signal obtained is suitable for ambulatory diagnosis.

Discrete Wavelet Transform (DWT), cardiac signal, threshold method, SNR, MSE, R interval.

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