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Effect of photic stimulation for migraine detection using random forest and discrete wavelet transform
Effat Univ, Saudi Arabia.
Effat Univ, Saudi Arabia.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, Faculty of Science & Engineering.
Univ Toronto, Canada.
2019 (English)In: Biomedical Signal Processing and Control, ISSN 1746-8094, E-ISSN 1746-8108, Vol. 49, p. 231-239Article in journal (Refereed) Published
Abstract [en]

Migraine is a neurological disorder characterized by persisting attacks, underlined by the sensitivity to light. One of the leading reasons that make migraine a bigger issue is that it cannot be diagnosed easily by physicians because of the numerous overlapping symptoms with other diseases, such as epilepsy and tension-headache. Consequently, studies have been growing on how to make a computerized decision support system for diagnosis of migraine. In most laboratory studies, flash stimulation is used during the recording of electroencephalogram (EEG) signals with different frequencies and variable (seconds) time windows. The main contribution of this study is the investigation of the effects of flash stimulation on the classification accuracy, and how to find the effective window length for EEG signal classification. To achieve this, we tested different machine learning algorithms on the EEG signals features extracted by using discrete wavelet transform. Our tests on the real-world dataset, recorded in the laboratory, show that the flash stimulation can improve the classification accuracy for more than 10%. Not surprisingly, it is seen that the same holds for the selection of time window length, i.e. the selection of the proper window length is crucial for the accurate migraine identification. (C) 2018 Elsevier Ltd. All rights reserved.

Place, publisher, year, edition, pages
ELSEVIER SCI LTD , 2019. Vol. 49, p. 231-239
Keywords [en]
Medical decision support system; Migraine; Electroencephalogram (EEG); Discrete wavelet transform (DWT); Machine learning algorithms
National Category
Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-154989DOI: 10.1016/j.bspc.2018.12.011ISI: 000458942500021OAI: oai:DiVA.org:liu-154989DiVA, id: diva2:1297557
Available from: 2019-03-20 Created: 2019-03-20 Last updated: 2019-03-20

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CiteExportLink to record
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Cite
Citation style
  • apa
  • harvard1
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • oxford
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf