Automatic SLEEP staging: From young aduslts to elderly patients using multi-class support vector machine

Jacob Kempfner, Poul Jennum, Helge B. D. Sorensen, Julie A. E. Christensen, Miki Nikolic

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

Abstract

Aging is a process that is inevitable, and makes our body vulnerable to age-related diseases. Age is the most consistent factor affecting the sleep structure. Therefore, new automatic sleep staging methods, to be used in both of young and elderly patients, are needed. This study proposes an automatic sleep stage detector, which can separate wakefulness, rapid-eye-movement (REM) sleep and non-REM (NREM) sleep using only EEG and EOG. Most sleep events, which define the sleep stages, are reduced with age. This is addressed by focusing on the amplitude of the clinical EEG bands, and not the affected sleep events. The age-related influences are then reduced by robust subject-specific scaling. The classification of the three sleep stages are achieved by a multi-class support vector machine using the one-versus-rest scheme. It was possible to obtain a high classification accuracy of 0.91. Validation of the sleep stage detector in other sleep disorders, such as apnea and narcolepsy, should be considered in future work.
Original languageEnglish
Title of host publication2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
PublisherIEEE
Publication date2013
Pages5777 - 5780
ISBN (Print)9781457702167
DOIs
Publication statusPublished - 2013
Event2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society - Oaska International Convention Center, Osaka, Japan
Duration: 3 Jul 20137 Jul 2013

Conference

Conference2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
LocationOaska International Convention Center
CountryJapan
CityOsaka
Period03/07/201307/07/2013

Keywords

  • diseases
  • electro-oculography
  • electroencephalography
  • geriatrics
  • medical disorders
  • medical signal detection
  • signal classification
  • sleep
  • support vector machines
  • Engineered Materials, Dielectrics and Plasmas

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