Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals

To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a...

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Authors: Melia, Umberto, Clarià Sancho, Francisco, Vallverdú, Montserrat, Caminal Magrans, Pere
Format: article
Status:Versión aceptada para publicación
Publication Date:2014
Country:España
Institution:Universitat de Lleida (UdL)
Repository:Repositori Obert UdL
OAI Identifier:oai:repositori.udl.cat:10459.1/47798
Online Access:https://doi.org/10.1016/j.medengphy.2013.11.014
http://hdl.handle.net/10459.1/47798
Access Level:Open access
Keyword:Procesado de señales biomédicas
Ingeniería biomédica
Biomedical signal processing
Electroencephalography
Digital filters
Enginyeria biomèdica
Biomedical engineering
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spelling Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signalsMelia, UmbertoClarià Sancho, FranciscoVallverdú, MontserratCaminal Magrans, PereProcesado de señales biomédicasIngeniería biomédicaBiomedical signal processingElectroencephalographyDigital filtersEnginyeria biomèdicaBiomedical engineeringTo remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient, mean of coherence function, and rate of absolute error. All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with > 0.85, C > 0.8, and RAE < 0.5. These values were significantly better than the performance of LMS adaptive filter ( < 0.85, C < 0.6, and RAE > 1).Elsevier2014info:eu-repo/semantics/articleinfo:eu-repo/semantics/acceptedVersionapplication/pdfhttps://doi.org/10.1016/j.medengphy.2013.11.014http://hdl.handle.net/10459.1/47798reponame:Repositori Obert UdL instname:Universitat de Lleida (UdL)InglésVersió postprint del document publicat a: https://doi.org/10.1016/j.medengphy.2013.11.014Medical Engineering & Physics, 2014, vol. 36, num. 4, p. 547-553cc-by-nc-nd (c) Elsevier, 2014info:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-nd/4.0/oai:repositori.udl.cat:10459.1/477982026-06-24T12:42:17Z
dc.title.none.fl_str_mv Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
title Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
spellingShingle Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
Melia, Umberto
Procesado de señales biomédicas
Ingeniería biomédica
Biomedical signal processing
Electroencephalography
Digital filters
Enginyeria biomèdica
Biomedical engineering
title_short Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
title_full Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
title_fullStr Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
title_full_unstemmed Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
title_sort Filtering and thresholding the analytic signal envelope in order to improve peak and spike noise reduction in EEG signals
dc.creator.none.fl_str_mv Melia, Umberto
Clarià Sancho, Francisco
Vallverdú, Montserrat
Caminal Magrans, Pere
author Melia, Umberto
author_facet Melia, Umberto
Clarià Sancho, Francisco
Vallverdú, Montserrat
Caminal Magrans, Pere
author_role author
author2 Clarià Sancho, Francisco
Vallverdú, Montserrat
Caminal Magrans, Pere
author2_role author
author
author
dc.subject.none.fl_str_mv Procesado de señales biomédicas
Ingeniería biomédica
Biomedical signal processing
Electroencephalography
Digital filters
Enginyeria biomèdica
Biomedical engineering
topic Procesado de señales biomédicas
Ingeniería biomédica
Biomedical signal processing
Electroencephalography
Digital filters
Enginyeria biomèdica
Biomedical engineering
description To remove peak and spike artifacts in biological time series has represented a hard challenge in the last decades. Several methods have been implemented mainly based on adaptive filtering in order to solve this problem. This work presents an algorithm for removing peak and spike artifacts based on a threshold built on the analytic signal envelope. The algorithm was tested on simulated and real EEG signals that contain peak and spike artifacts with random amplitude and frequency occurrence. The performance of the filter was compared with commonly used adaptive filters. Three indexes were used for testing the performance of the filters: Correlation coefficient, mean of coherence function, and rate of absolute error. All these indexes were calculated between filtered signal and original signal without noise. It was found that the new proposed filter was able to reduce the amplitude of peak and spike artifacts with > 0.85, C > 0.8, and RAE < 0.5. These values were significantly better than the performance of LMS adaptive filter ( < 0.85, C < 0.6, and RAE > 1).
publishDate 2014
dc.date.none.fl_str_mv 2014
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/acceptedVersion
format article
status_str acceptedVersion
dc.identifier.none.fl_str_mv https://doi.org/10.1016/j.medengphy.2013.11.014
http://hdl.handle.net/10459.1/47798
url https://doi.org/10.1016/j.medengphy.2013.11.014
http://hdl.handle.net/10459.1/47798
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv Versió postprint del document publicat a: https://doi.org/10.1016/j.medengphy.2013.11.014
Medical Engineering & Physics, 2014, vol. 36, num. 4, p. 547-553
dc.rights.none.fl_str_mv cc-by-nc-nd (c) Elsevier, 2014
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-nd/4.0/
rights_invalid_str_mv cc-by-nc-nd (c) Elsevier, 2014
http://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:Repositori Obert UdL
instname:Universitat de Lleida (UdL)
instname_str Universitat de Lleida (UdL)
reponame_str Repositori Obert UdL
collection Repositori Obert UdL
repository.name.fl_str_mv
repository.mail.fl_str_mv
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