Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram
(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.
(Less)
- author
- Bhattacharya, Avik ; Sarkar, Anasua LU and Basak, Piyali
- publishing date
- 2017-06-22
- 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}}, }