Microsleep Detection in Electrophysiological Signals

Martin Golz, David Sommer, Danilo Mandic


An adaptive biosignal analysis system for the detection of microsleep events is presented. The system was applied to the electroencephalogram and electrooculogram recorded of 23 young volunteers while performing monotonic overnight driving in our real car driving simulation laboratory. Biosignals during clear observable microsleep and non-microsleep events were processed and classified. Besides the commonly applied Periodogram method to estimate power spectral densities we utilized the recently established method of Delay Vector Variance. The obtained feature set was used as input vectors of populations of Learning Vector Quantization networks which were evolved by Genetic Algorithms. The results were compared with results from best performing Support Vector Machines. Fusion of all recorded signals and of both types of features led to empirical test errors down to 11.2 %. It is shown that the proposed methodology is able to detect, but not to predict immediately oncoming events.


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Paper Citation

in Harvard Style

Golz M., Sommer D. and Mandic D. (2005). Microsleep Detection in Electrophysiological Signals . In Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005) ISBN 972-8865-35-X, pages 102-109. DOI: 10.5220/0001195701020109

in Bibtex Style

author={Martin Golz and David Sommer and Danilo Mandic},
title={Microsleep Detection in Electrophysiological Signals},
booktitle={Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)},

in EndNote Style

JO - Proceedings of the 1st International Workshop on Biosignal Processing and Classification - Volume 1: BPC, (ICINCO 2005)
TI - Microsleep Detection in Electrophysiological Signals
SN - 972-8865-35-X
AU - Golz M.
AU - Sommer D.
AU - Mandic D.
PY - 2005
SP - 102
EP - 109
DO - 10.5220/0001195701020109