Contamination in Electromyography Signals and Noise Removal Techniques

Doctor of Philosophy (Electrical Engineering), 2019

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Bibliographic Details
Main Author:	Thandar Oo
Other Authors:	Pornchai Phukpattaranont
Format:	Theses and Dissertations
Language:	English
Published:	Prince of Songkla University 2024
Subjects:	Electromyography Noise
Online Access:	http://kb.psu.ac.th/psukb/handle/2016/19584
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Institution:	Prince of Songkhla University
Language:	English

id	th-psu.2016-19584
record_format	dspace
spelling	th-psu.2016-195842024-07-25T08:38:11Z Contamination in Electromyography Signals and Noise Removal Techniques Thandar Oo Pornchai Phukpattaranont Faculty of Engineering Electrical Engineering คณะวิศวกรรมศาสตร์ ภาควิชาวิศวกรรมไฟฟ้า Electromyography Noise Doctor of Philosophy (Electrical Engineering), 2019 The electromyography (EMG) signal can be contaminated with noise during data collection. For example, when the EMG signal is acquired from muscles in the torso, the electrocardiography (ECG) signal coming from heart activity can interfere. In this thesis, we proposed a novel method on noise removal and the signal- to-noise ratio (SNR) estimation algorithms. For the noise removal method, a technique based on discrete stationary wavelet transform (DSWT) is proposed to remove ECG interference from the EMG signal while taking into account the SNR. The contaminated EMG signal is decomposed using 5-level DSWT with the Symlet wavelet function. A clean EMG signal can then be obtained by inverse DSWT mapping of the new thresholded coefficients. The performance based on mean absolute error, correlation coefficient, and relative error shows that the DSWT method is better than a high-pass filter. For the SNR estimation method, we present a novel SNR estimation in the EMG signal contaminated with the ECG interference. We calculate the features from the EMG signals. Then, the features are used as an input of a neural network (NN). The NN output is an SNR estimate. The results showed that the waveform length was the best feature for estimating SNR. It gave the highest average correlation coefficient at 0.9663. These results suggested that the waveform length was able to be deployed not only in an EMG recognition system but also in an EMG signal quality measurement when the EMG signal was contaminated with the ECG interference. 2024-07-25T08:38:11Z 2024-07-25T08:38:11Z 2019 Thesis http://kb.psu.ac.th/psukb/handle/2016/19584 en Attribution-NonCommercial-NoDerivs 3.0 Thailand http://creativecommons.org/licenses/by-nc-nd/3.0/th/ application/pdf Prince of Songkla University
institution	Prince of Songkhla University
building	Khunying Long Athakravi Sunthorn Learning Resources Center
continent	Asia
country	Thailand Thailand
content_provider	Khunying Long Athakravi Sunthorn Learning Resources Center
collection	PSU Knowledge Bank
language	English
topic	Electromyography Noise
spellingShingle	Electromyography Noise Thandar Oo Contamination in Electromyography Signals and Noise Removal Techniques
description	Doctor of Philosophy (Electrical Engineering), 2019
author2	Pornchai Phukpattaranont
author_facet	Pornchai Phukpattaranont Thandar Oo
format	Theses and Dissertations
author	Thandar Oo
author_sort	Thandar Oo
title	Contamination in Electromyography Signals and Noise Removal Techniques
title_short	Contamination in Electromyography Signals and Noise Removal Techniques
title_full	Contamination in Electromyography Signals and Noise Removal Techniques
title_fullStr	Contamination in Electromyography Signals and Noise Removal Techniques
title_full_unstemmed	Contamination in Electromyography Signals and Noise Removal Techniques
title_sort	contamination in electromyography signals and noise removal techniques
publisher	Prince of Songkla University
publishDate	2024
url	http://kb.psu.ac.th/psukb/handle/2016/19584
_version_	1806509648374661120

Contamination in Electromyography Signals and Noise Removal Techniques

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