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 proceedingConference contribution

4 Citations (Scopus)


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 languageEnglish
Title of host publication2017 IEEE Life Sciences Conference, LSC 2017
Number of pages4
ISBN (Electronic)9781538610305
Publication statusPublished - 23 Jan 2018
Externally publishedYes

Bibliographical note

Publisher Copyright:
© 2017 IEEE.

ASJC Scopus subject areas

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


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