On the identification of sleep stages in mouse electroencephalography time-series

Journal of Neuroscience Methods
Thomas LampertBettina Platt

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...Continue Reading

References

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Jan 18, 2011·Behavioural Brain Research·Bettina Platt, Gernot Riedel
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Citations

Apr 29, 2016·International Journal of Neural Systems·Farid YaghoubySridhar Sunderam

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