title: On the identification of sleep stages in mouse electroencephalography time-series.
abstract: The automatic identification of sleep stages in electroencephalography (EEG) time-series is a long desired goal for researchers concerned with the study of sleep disorders. This paper presents advances towards achieving this goal, with particular application to EEG time-series recorded from mice. Approaches in the literature apply supervised learning classifiers, however, these do not reach the performance levels required for use within a laboratory. In this paper, detection reliability is increased, most notably in the case of REM stage identification, by naturally decomposing the problem and applying a support vector machine (SVM) based classifier to each of the EEG channels. Their outputs are integrated within a multiple classifier system. Furthermore, there exists no general consensus on the ideal choice of parameter values in such systems. Therefore, an investigation into the effects upon the classification performance is presented by varying parameters such as the epoch length; features size; number of training samples; and the method for calculating the power spectral density estimate. Finally, the results of these investigations are brought together to demonstrate the performance of the proposed classification algorithm in two cases: intra-animal classification and inter-animal classification. It is shown that, within a dataset of 10 EEG recordings, and using less than 1% of an EEG as training data, a mean classification errors of Awake 6.45%, NREM 5.82%, and REM 6.65% (with standard deviations less than 0.6%) are achieved in intra-animal analysis and, when using the equivalent of 7% of one EEG as training data, Awake 10.19%, NREM 7.75%, and REM 17.43% are achieved in inter-animal analysis (with mean standard deviations of 6.42%, 2.89%, and 9.69% respectively). A software package implementing the proposed approach will be made available through Cybula Ltd.
authors.affiliation: Department of Computer Science, University of York, York YO10 5GH, UK; Cybula Ltd., Department of Computer Science, York YO10 5GH, UK. Electronic address: tomalampert@hotmail.com.
authors.forename: Thomas
authors.initials: T
authors.lastname: Lampert
authors.affiliation: School of Medical Sciences, College of Life Sciences and Medicine, University of Aberdeen, Institute of Medical Sciences, Aberdeen AB25 2ZD, UK. Electronic address: a.plano@abdn.ac.uk.
authors.forename: Andrea
authors.initials: A
authors.lastname: Plano
authors.affiliation: Department of Computer Science, University of York, York YO10 5GH, UK; Cybula Ltd., Department of Computer Science, York YO10 5GH, UK. Electronic address: austin@cs.york.ac.uk.
authors.forename: Jim
authors.initials: J
authors.lastname: Austin
authors.affiliation: School of Medical Sciences, College of Life Sciences and Medicine, University of Aberdeen, Institute of Medical Sciences, Aberdeen AB25 2ZD, UK. Electronic address: b.platt@abdn.ac.uk.
authors.forename: Bettina
authors.initials: B
authors.lastname: Platt
mesh_terms: D000818:Animals; D001143:Arousal; D058256:Brain Waves / Q000502:physiology*; D015331:Cohort Studies; D004569:Electroencephalography; D004576:Electromyography; D005260:Female; D008297:Male; D051379:Mice; D012894:Sleep Stages / Q000502:physiology*; D013057:Spectrum Analysis; D060388:Support Vector Machine; D013997:Time Factors
keywords: EEG; Fourier transform; Multi-classifier system; Sleep stage; Support vector machine; Time-series; Welch transform