Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram

Avik Bhattacharya; Anasua Sarkar; Piyali Basak

doi:10.1109/SCOPES.2016.7955630

Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram

Avik Bhattacharya, Anasua Sarkar, Piyali Basak

Jadavpur University

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

Sammanfattning

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.

Originalspråk	engelska
Titel på värdpublikation	International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings
Förlag	IEEE - Institute of Electrical and Electronics Engineers Inc.
Sidor	1197-1201
Antal sidor	5
ISBN (elektroniskt)	9781509046201
DOI	https://doi.org/10.1109/SCOPES.2016.7955630
Status	Published - 2017 juni 22
Externt publicerad	Ja
Evenemang	2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Paralakhernundi, Odisha, Indien Varaktighet: 2016 okt. 3 → 2016 okt. 5

Konferens

Konferens	2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016
Land/Territorium	Indien
Ort	Paralakhernundi, Odisha
Period	2016/10/03 → 2016/10/05

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10.1109/SCOPES.2016.7955630

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Bhattacharya, A., Sarkar, A., & Basak, P. (2017). Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram. I International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings (s. 1197-1201). Artikel 7955630 IEEE - Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/SCOPES.2016.7955630

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title = "Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram",

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 varying levels of decomposition. Hybrid thresholding is the best noise estimation and cancellation technique. Wavelet functions with more oscillations produce good denoising than others.",

keywords = "Biomedical signal processing, Electrocardiogram, EMG noise, Noise reduction, Wavelet denoising, Wavelet thresholding",

author = "Avik Bhattacharya and Anasua Sarkar and Piyali Basak",

year = "2017",

month = jun,

day = "22",

doi = "10.1109/SCOPES.2016.7955630",

language = "English",

pages = "1197--1201",

booktitle = "International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings",

publisher = "IEEE - Institute of Electrical and Electronics Engineers Inc.",

address = "United States",

note = "2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 ; Conference date: 03-10-2016 Through 05-10-2016",

}

Bhattacharya, A, Sarkar, A & Basak, P 2017, Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram. i International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings., 7955630, IEEE - Institute of Electrical and Electronics Engineers Inc., s. 1197-1201, 2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016, Paralakhernundi, Odisha, Indien, 2016/10/03. https://doi.org/10.1109/SCOPES.2016.7955630

Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram. / Bhattacharya, Avik; Sarkar, Anasua; Basak, Piyali.
International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings. IEEE - Institute of Electrical and Electronics Engineers Inc., 2017. s. 1197-1201 7955630.

Forskningsoutput: Kapitel i bok/rapport/Conference proceeding › Konferenspaper i proceeding › Peer review

TY - GEN

T1 - Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram

AU - Bhattacharya, Avik

AU - Sarkar, Anasua

AU - Basak, Piyali

PY - 2017/6/22

Y1 - 2017/6/22

N2 - 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.

AB - 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.

KW - Biomedical signal processing

KW - Electrocardiogram

KW - EMG noise

KW - Noise reduction

KW - Wavelet denoising

KW - Wavelet thresholding

U2 - 10.1109/SCOPES.2016.7955630

DO - 10.1109/SCOPES.2016.7955630

M3 - Paper in conference proceeding

AN - SCOPUS:85025142497

SP - 1197

EP - 1201

BT - International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016 - Proceedings

PB - IEEE - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Signal Processing, Communication, Power and Embedded System, SCOPES 2016

Y2 - 3 October 2016 through 5 October 2016

ER -

Quality evaluation of wavelet functions for myopulse suppression in electrocardiogram

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