Outlier removal in facial surface electromyography through Hampel filtering technique

Susmit Bhowmik; Beth Jelfs; Sridhar P. Arjunan; Dinesh K. Kumar

doi:10.1109/LSC.2017.8268192

Outlier removal in facial surface electromyography through Hampel filtering technique

Susmit Bhowmik, Beth Jelfs, Sridhar P. Arjunan, Dinesh K. Kumar

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

4 Citations (Scopus)

Abstract

The aim of the present investigation was to filter outliers in facial surface electromyography (fSEMG) originating from eye blinks, through a decision based filtering technique. Since, these outliers lie within the frequency range of electromyographic activity (30-300 Hz), conventional filtering methods fail to remove them. Hence, an application of an outlier filtering technique, Hampel filtering, has been introduced which is proficient at removing high frequency impulsive spikes (100-150 Hz) from facial sEMG. The Hampel filter removes the outliers without distorting the original data sequence and improves the quality of the signal as observed in time-frequency analysis.

Original language	English
Title of host publication	2017 IEEE Life Sciences Conference, LSC 2017
Publisher	IEEE
Pages	258-261
Number of pages	4
ISBN (Electronic)	9781538610305
DOIs	https://doi.org/10.1109/LSC.2017.8268192
Publication status	Published - 23 Jan 2018
Externally published	Yes

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

Health Informatics
Artificial Intelligence
Human-Computer Interaction
Biomedical Engineering
Medicine (miscellaneous)

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/LSC.2017.8268192

Cite this

@inproceedings{e923232d8b2e4c4fb6140d2b5b0ffcf2,

title = "Outlier removal in facial surface electromyography through Hampel filtering technique",

abstract = "The aim of the present investigation was to filter outliers in facial surface electromyography (fSEMG) originating from eye blinks, through a decision based filtering technique. Since, these outliers lie within the frequency range of electromyographic activity (30-300 Hz), conventional filtering methods fail to remove them. Hence, an application of an outlier filtering technique, Hampel filtering, has been introduced which is proficient at removing high frequency impulsive spikes (100-150 Hz) from facial sEMG. The Hampel filter removes the outliers without distorting the original data sequence and improves the quality of the signal as observed in time-frequency analysis.",

author = "Susmit Bhowmik and Beth Jelfs and Arjunan, {Sridhar P.} and Kumar, {Dinesh K.}",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.",

year = "2018",

month = jan,

day = "23",

doi = "10.1109/LSC.2017.8268192",

language = "English",

pages = "258--261",

booktitle = "2017 IEEE Life Sciences Conference, LSC 2017",

publisher = "IEEE",

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TY - GEN

T1 - Outlier removal in facial surface electromyography through Hampel filtering technique

AU - Bhowmik, Susmit

AU - Jelfs, Beth

AU - Arjunan, Sridhar P.

AU - Kumar, Dinesh K.

PY - 2018/1/23

Y1 - 2018/1/23

N2 - The aim of the present investigation was to filter outliers in facial surface electromyography (fSEMG) originating from eye blinks, through a decision based filtering technique. Since, these outliers lie within the frequency range of electromyographic activity (30-300 Hz), conventional filtering methods fail to remove them. Hence, an application of an outlier filtering technique, Hampel filtering, has been introduced which is proficient at removing high frequency impulsive spikes (100-150 Hz) from facial sEMG. The Hampel filter removes the outliers without distorting the original data sequence and improves the quality of the signal as observed in time-frequency analysis.

AB - The aim of the present investigation was to filter outliers in facial surface electromyography (fSEMG) originating from eye blinks, through a decision based filtering technique. Since, these outliers lie within the frequency range of electromyographic activity (30-300 Hz), conventional filtering methods fail to remove them. Hence, an application of an outlier filtering technique, Hampel filtering, has been introduced which is proficient at removing high frequency impulsive spikes (100-150 Hz) from facial sEMG. The Hampel filter removes the outliers without distorting the original data sequence and improves the quality of the signal as observed in time-frequency analysis.

UR - http://www.scopus.com/inward/record.url?scp=85046266355&partnerID=8YFLogxK

U2 - 10.1109/LSC.2017.8268192

DO - 10.1109/LSC.2017.8268192

M3 - Conference contribution

AN - SCOPUS:85046266355

SP - 258

EP - 261

BT - 2017 IEEE Life Sciences Conference, LSC 2017

PB - IEEE

ER -

Outlier removal in facial surface electromyography through Hampel filtering technique

Abstract

Bibliographical note

ASJC Scopus subject areas

UN SDGs

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Cite this