A framework for automatic relevance determination based on artificial neural networks and evolution strategy is presented. It is applied for an important problem in biomedicine, namely the detection of unintentional episodes of sleep during sustained operations of subjects, so-called microsleep episodes. Human expert ratings based on video and biosignal recordings are necessary to judge microsleep episodes. Ratings are fused together with linear and nonlinear features which are extracted from three types of biosignals: electroencephalography, electrooculography, and eyetracking. Changes in signal modality due to nonlinearity and stochasticity are quantified by the ‘delay vector variance’ method. Results show large inter-individual variability. Though the framework is outperformed by support vector machines in terms of classi- fication accuracy, the estimated relevance values provide knowledge of signal characteristics during microsleep episodes.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Akerstedt, T., Kecklund, G., Knutsson, A.: Manifest sleepiness and the spectral content of the EEG during shift work. Sleep 14, 221–225 (1991)
Cajochen, C.: Power density in theta/alpha frequencies of the waking EEG progressively increases during sustained wakefulness. Sleep 18, 890–894 (1995)
Cortes, C., Vapnik, V.: Support vector networks. Mach Learn 20, 273–297 (1995)
Dinges, D.: An overview of sleepiness and accidents. J Sleep Res. 4(Suppl 2), 4–14 (1995)
Gautama, T., Mandic, D., VanHulle, M.: The delay vector variance method for detecting determinism and nonlinearity in time series. Physica D 190, 167–176 (2004)
Golz, M., Sommer, D.: The performance of LVQ based automatic relevance determination applied to spontaneous biosignals. In: Gabrys et al. (eds.) Proceedings of the 10th International Conference KES-2006, LNAI, vol. 4253, pp. 1256–1263. Springer, Berlin Heidelberg New York (2006)
Golz, M., Sommer, D., Chen, M., Trutschel, U., Mandic, D.: Feature fusion for the detection of microsleep events. J VLSI Signal Proc 49, 329–342 (2007)
Golz, M., Sommer, D., Seyfarth, A., Trutschel, U., Moore-Ede, M.: Application of vector-based neural networks for the recognition of beginning microsleep episodes with an eyetracking system. In: Kuncheva et al. (eds.) Computational Intelligence: Methods and Applications, pp. 130–134. ICSC Academic, Canada (2001)
Horne, J., Baulk, S.: Awareness of sleepiness when driving. Psychophysiology 41, 161–165 (2004)
Ingre, M., Åkerstedt, T., Peters, Anund, Kecklund: Subjective sleepiness, simulated driving performance and blink duration. J Sleep Res. 15, 47–53 (2006)
Knipling, R.: Individual differences in commercial driver fatigue susceptibility: Evidence and implications. In: Proceedings of the International Conference on Fatigue Management in Transportation Operations. Seattle (2005)
Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer, Berlin Heidelberg New York (2001)
Lal, S., Craig, A.: Driver fatigue: Electroencephalography and psychological assessment. Psychophysiology 39, 313–321 (2002)
Langwieder, K., Sporner, A., Hell, W.: Einschlafen am Steuer: Hauptursache schwerer Verkehrsunfälle. Wien Klin Wochenschr 145, 473 (1995)
Makeig, S., Jung, T.: Changes in alertness are a principal component of variance in the EEG spectrum. Neuroreport 7, 213–216 (1995)
Makeig, S., Jung, T., Sejnowski, T.: Awareness during drowsiness: Dynamics and electrophysiological correlates. Can J Exp Psychol 54, 266–273 (2000)
Maulsby, R., et al.: The normative electroencephalographic data reference library. Final report, National Aeronautics and Space Administration (1968)
Santamaria, J., Chiappa, K.: The EEG of drowsiness in normal adults. J Clin Neurophysiol 4, 327–382 (1987)
Schleicher, R., Galley, N., Briest, S., Galley, L.: Looking tired? Blinks and saccades as indicators of fatigue. Ergonomics, in press (2008)
Schwefel, H., Rudolph, G.: Contemporary evolution strategies. In: Proceedings of the 3rd European Conference on Artificial Life, vol. 929, pp. 893–907. Springer, Berlin (1995)
Sommer, D., Golz, M., Trutschel, U., Mandic, D.: Fusion of state space and frequency-domain features for improved microsleep detection. In: W. Duch, et al. (eds.) Proceedings of the ICANN, LNCS, vol. 3697, pp. 753–759. Springer, Berlin Heidelberg New York (2005)
Sommer, D., Hink, T., Golz, M.: Application of learning vector quantization to detect drivers dozing-off. In: Proceedings of the 6th World Multiconf Systemics, Cybernetics and Informatics (ISAS-SCI2002), vol. XI, pp. 95–98. Orlando, USA (2002)
Summala, H., Häkkänen, H., Mikkola, T., Sinkkonen, J.: Task effects on fatigue symptoms in overnight driving. Ergonomics 42, 798–806 (1999)
Trutschel, U., Guttkuhn, R., Ramsthaler, C., Golz, M., Moore-Ede, M.: Automatic detection of microsleep events using a neuro-fuzzy hybrid system. In: Proceedings of the EUFIT-98, vol. 3, pp. 1762–1766. Aachen, Germany (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2008 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
Golz, M., Sommer, D. (2008). Automatic Knowledge Extraction: Fusion of Human Expert Ratings and Biosignal Features for Fatigue Monitoring Applications. In: Mandic, D., Golz, M., Kuh, A., Obradovic, D., Tanaka, T. (eds) Signal Processing Techniques for Knowledge Extraction and Information Fusion. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-74367-7_16
Download citation
DOI: https://doi.org/10.1007/978-0-387-74367-7_16
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-74366-0
Online ISBN: 978-0-387-74367-7
eBook Packages: EngineeringEngineering (R0)