Automatic detection and classification of artifacts in single-channel EEG

Thomas Olund, Jonas Duun-Henriksen, Troels W. Kjaer, Helge Bjarup Dissing Sørensen

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

Ambulatory EEG monitoring can provide medical doctors important diagnostic information, without hospitalizing the patient. These recordings are however more exposed to noise and artifacts compared to clinically recorded EEG. An automatic artifact detection and classification algorithm for single-channel EEG is proposed to help identifying these artifacts. Features are extracted from the EEG signal and wavelet subbands. Subsequently a selection algorithm is applied in order to identify the best discriminating features. A non-linear support vector machine is used to discriminate among different artifact classes using the selected features. Single-channel (Fp1-F7) EEG recordings are obtained from experiments with 12 healthy subjects performing artifact inducing movements. The dataset was used to construct and validate the model. Both subject-specific and generic implementation, are investigated. The detection algorithm yield an average sensitivity and specificity above 95% for both the subject-specific and generic models. The classification algorithm show a mean accuracy of 78 and 64% for the subject-specific and generic model, respectively. The classification model was additionally validated on a reference dataset with similar results.
Original languageEnglish
Title of host publication2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Publication date2014
Pages922-925
ISBN (Print)978-1-4244-7929-0
DOIs
Publication statusPublished - 2014
Event36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Chicago, IL, United States
Duration: 26 Aug 201430 Aug 2014
Conference number: 36
http://embc.embs.org/2014/

Conference

Conference36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Number36
CountryUnited States
CityChicago, IL
Period26/08/201430/08/2014
Internet address

Keywords

  • Bioengineering
  • Accuracy
  • Brain modeling
  • Electroencephalography
  • Feature extraction
  • Magnetic heads
  • Muscles
  • Support vector machines

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