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Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm

Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Biomedical Engineering Letters Springer Journals

Adaptive filtering method for EMG signal using bounded range artificial bee colony algorithm

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Publisher
Springer Journals
Copyright
Copyright © 2017 by Korean Society of Medical and Biological Engineering and Springer-Verlag GmbH Germany, part of Springer Nature
Subject
Engineering; Biomedical Engineering; Biological and Medical Physics, Biophysics; Biomedicine, general; Medical and Radiation Physics
ISSN
2093-9868
eISSN
2093-985X
DOI
10.1007/s13534-017-0056-x
Publisher site
See Article on Publisher Site

Abstract

In this paper, an adaptive artefact canceller is designed using the bounded range artificial bee colony (BR-ABC) optimization technique. The results of proposed method are compared with recursive least square and other evolutionary algorithms. The performance of these algorithms is evaluated in terms of signal-to-noise ratio (SNR), mean square error (MSE), maximum error (ME) mean, standard deviation (SD) and correlation factor (r). The noise attenuation capability is tested on EMG signal contaminated with power line and ECG noise at different SNR levels. A comparative study of various techniques reveals that the performance of BR-ABC algorithm is better in noisy environment. Our simulation results show that the ANC filter using BR-ABC technique provides 15 dB improvement in output average SNR, 63 and 83% reduction in MSE and ME, respectively as compared to ANC filter based on PSO technique. Further, the ANC filter designed using BR-ABC technique enhances the correlation between output and pure EMG signal.

Journal

Biomedical Engineering Letters – Springer Journals

Published: Dec 28, 2017

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