Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal

M. Radha, G. Garcia Molina, M. Poel, G. Tononi

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

33 Citations (Scopus)

Abstract

Automatic sleep staging on an online basis has recently emerged as a research topic motivated by fundamental sleep research. The aim of this paper is to find optimal signal processing methods and machine learning algorithms to achieve online sleep staging on the basis of a single EEG signal. The classification performance obtained using six different EEG signals and various signal processing feature sets is compared using the kappa statistic which has very recently become popular in sleep staging research. A variable duration of the EEG segment (or epoch) to decide on the sleep stage is also analyzed. Spectral-domain, time-domain, linear, and nonlinear features are compared in terms of performance and two types of machine learning approaches (random forests and support vector machines) are assessed. We have determined that frontal EEG signals, with spectral linear features, epoch durations between 18 and 30 seconds, and a random forest classifier lead to optimal classification performance while ensuring real-time online operation.
Original languageEnglish
Title of host publicationProceedings of the 36th Annual Internation Conference of the IEE Engineering in Medicine and Biology Society
Pages1876-1880
DOIs
Publication statusPublished - 26 Aug 2014
Event36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014) - Chicago, United States
Duration: 26 Aug 201430 Aug 2014
Conference number: 36

Conference

Conference36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2014)
Abbreviated titleEMBC 2014
Country/TerritoryUnited States
CityChicago
Period26/08/1430/08/14

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