Research on AR-AKF Model Denoising of the EMG Signal

Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the...

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Autores principales:	Sijia Chen, Zhizeng Luo, Tong Hua
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer applications to medicine. Medical informatics R858-859.7
Acceso en línea:	https://doaj.org/article/4044702b6e1144bfaec872705cc95461
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spelling	oai:doaj.org-article:4044702b6e1144bfaec872705cc954612021-11-22T01:11:17ZResearch on AR-AKF Model Denoising of the EMG Signal1748-671810.1155/2021/9409560https://doaj.org/article/4044702b6e1144bfaec872705cc954612021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/9409560https://doaj.org/toc/1748-6718Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages.Sijia ChenZhizeng LuoTong HuaHindawi LimitedarticleComputer applications to medicine. Medical informaticsR858-859.7ENComputational and Mathematical Methods in Medicine, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer applications to medicine. Medical informatics R858-859.7
spellingShingle	Computer applications to medicine. Medical informatics R858-859.7 Sijia Chen Zhizeng Luo Tong Hua Research on AR-AKF Model Denoising of the EMG Signal
description	Electromyography (EMG) signals can be used for clinical diagnosis and biomedical applications. It is very important to reduce noise and to acquire accurate signals for the usage of the EMG signals in biomedical engineering. Since EMG signal noise has the time-varying and random characteristics, the present study proposes an adaptive Kalman filter (AKF) denoising method based on an autoregressive (AR) model. The AR model is built by applying the EMG signal, and the relevant parameters are integrated to find the state space model required to optimally estimate AKF, eliminate the noise in the EMG signal, and restore the damaged EMG signal. To be specific, AR autoregressive dynamic modeling and repair for distorted signals are affected by noise, and AKF adaptively can filter time-varying noise. The denoising method based on the self-learning mechanism of AKF exhibits certain capabilities to achieve signal tracking and adaptive filtering. It is capable of adaptively regulating the model parameters in the absence of any prior statistical knowledge regarding the signal and noise, which is aimed at achieving a stable denoising effect. By comparatively analyzing the denoising effects exerted by different methods, the EMG signal denoising method based on the AR-AKF model is demonstrated to exhibit obvious advantages.
format	article
author	Sijia Chen Zhizeng Luo Tong Hua
author_facet	Sijia Chen Zhizeng Luo Tong Hua
author_sort	Sijia Chen
title	Research on AR-AKF Model Denoising of the EMG Signal
title_short	Research on AR-AKF Model Denoising of the EMG Signal
title_full	Research on AR-AKF Model Denoising of the EMG Signal
title_fullStr	Research on AR-AKF Model Denoising of the EMG Signal
title_full_unstemmed	Research on AR-AKF Model Denoising of the EMG Signal
title_sort	research on ar-akf model denoising of the emg signal
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/4044702b6e1144bfaec872705cc95461
work_keys_str_mv	AT sijiachen researchonarakfmodeldenoisingoftheemgsignal AT zhizengluo researchonarakfmodeldenoisingoftheemgsignal AT tonghua researchonarakfmodeldenoisingoftheemgsignal
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Research on AR-AKF Model Denoising of the EMG Signal

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