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Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram

Bhattacharya, Avik ; Sarkar, Anasua LU orcid and Basak, Piyali (2017) 2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 p.1197-1201
Abstract

ECG is susceptible to parasitic myopulses due to the overlapping frequency bandwidth of ECG and EMG. EMG signal has a bandwidth of about 20-500 Hz and overlaps with the ECG frequency range. i.e. 0.05-150 Hz. These interferences occur due to movement of muscles and respiratory actions during ECG recording. Removal of EMG noise from ECG is an important criterion for proper analysis of the signal. In this study, we evaluated the denoising performance of wavelet functions by considering SNR as the quality judgement parameter. DWT provides better denoising over traditional filtering techniques. The level of decomposition plays an important role in denoising quality. There is variation in the performance of hard and soft thresholding with... (More)

ECG is susceptible to parasitic myopulses due to the overlapping frequency bandwidth of ECG and EMG. EMG signal has a bandwidth of about 20-500 Hz and overlaps with the ECG frequency range. i.e. 0.05-150 Hz. These interferences occur due to movement of muscles and respiratory actions during ECG recording. Removal of EMG noise from ECG is an important criterion for proper analysis of the signal. In this study, we evaluated the denoising performance of wavelet functions by considering SNR as the quality judgement parameter. DWT provides better denoising over traditional filtering techniques. The level of decomposition plays an important role in denoising quality. There is variation in the performance of hard and soft thresholding with varying levels of decomposition. Hybrid thresholding is the best noise estimation and cancellation technique. Wavelet functions with more oscillations produce good denoising than others.

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Please use this url to cite or link to this publication:
author
; and
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
keywords
Biomedical signal processing, Electrocardiogram, EMG noise, Noise reduction, Wavelet denoising, Wavelet thresholding
host publication
International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings
article number
7955630
pages
5 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016
conference location
Paralakhernundi, Odisha, India
conference dates
2016-10-03 - 2016-10-05
external identifiers
  • scopus:85025142497
ISBN
9781509046201
DOI
10.1109/SCOPES.2016.7955630
language
English
LU publication?
no
id
8352dd94-50bd-43c7-8795-06133e3823c3
date added to LUP
2018-09-13 10:14:41
date last changed
2022-03-09 20:33:27
@inproceedings{8352dd94-50bd-43c7-8795-06133e3823c3,
  abstract     = {{<p>ECG is susceptible to parasitic myopulses due to the overlapping frequency bandwidth of ECG and EMG. EMG signal has a bandwidth of about 20-500 Hz and overlaps with the ECG frequency range. i.e. 0.05-150 Hz. These interferences occur due to movement of muscles and respiratory actions during ECG recording. Removal of EMG noise from ECG is an important criterion for proper analysis of the signal. In this study, we evaluated the denoising performance of wavelet functions by considering SNR as the quality judgement parameter. DWT provides better denoising over traditional filtering techniques. The level of decomposition plays an important role in denoising quality. There is variation in the performance of hard and soft thresholding with varying levels of decomposition. Hybrid thresholding is the best noise estimation and cancellation technique. Wavelet functions with more oscillations produce good denoising than others.</p>}},
  author       = {{Bhattacharya, Avik and Sarkar, Anasua and Basak, Piyali}},
  booktitle    = {{International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings}},
  isbn         = {{9781509046201}},
  keywords     = {{Biomedical signal processing; Electrocardiogram; EMG noise; Noise reduction; Wavelet denoising; Wavelet thresholding}},
  language     = {{eng}},
  month        = {{06}},
  pages        = {{1197--1201}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram}},
  url          = {{http://dx.doi.org/10.1109/SCOPES.2016.7955630}},
  doi          = {{10.1109/SCOPES.2016.7955630}},
  year         = {{2017}},
}