Abstract
In this paper, we analyze the EEG rhythms of subjects undergoing the cortical auditory evoked potential (CAEP) hearing test. Investigation of the importance of the different EEG rhythms in terms of their capability in differentiating between the different alertness states when considering 64 channel EEG montage is conducted. This is followed by considering subsets that contain 2, 3, 4 as well as all 5 EEG rhythms. Finally, a feature subset selection method based on differential evolution (DE) that has particularly been proposed to deal with multi-channel signals is used to search for the best subset of EEG rhythms for the various channels.
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
Martin, B., Tremblay, K., Stapells, D.: Principles and applications of cortical auditory evoked potentials. In: Burkard, R., Eggermont, J., Don, M. (eds.) Auditory Evoked Potentials: Basic Principles and Clinical Application, pp. 482–507. Lippincott Williams and Wilkins, Philadelphia (2007)
Campbell, K., Muller-Gass, A.: The extent of processing of near-hearing threshold stimuli during Nnatural sleep. Sleep 34(9), 1243–1249 (2011)
Varri, A., Hirvonen, K., Hasan, J., Loula, P., Haikkinen, V.: A computerized analysis system for vigilance studies. Computer Methods and Programs in Biomedicine 39, 113–124 (1992)
Nakamura, M., Sugi, T., Ikeda, A., Kakigi, R., Shibasaki, H.: Clinical application of automatic integrative interpretation of awake background. EEG: quantitative interpretation, report making, and detection of artifacts and reduced vigilance level. Electroencephalography and Clinical Neurophysiology 98, 103–112 (1996)
Vuckovic, A., Radivojevic, V., Chen, A.C.N., Popovic, D.: Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Medical Engineering and Physics 24, 349–360 (2002)
Kiymik, M.K., Akin, M., Subasi, A.: Automatic recognition of alertness level by using wavelet transform and artificial neural network. Journal of Neuroscience Methods 139, 231–240 (2004)
Kurt, M.B., Sezgin, N., Akin, M., Kirbas, G., Bayram, M.: The ANN-based computing of drowsy level. Expert Systems with Applications 36, 2534–2542 (2009)
Papadelis, C., Chen, Z., Kourtidou-Papadeli, C., Bamidis, P.D., Chouvarda, I., Bekiaris, E., Maglaveras, N.: Monitoring sleepiness with on-board electrophysiological recordings for preventing sleep-deprived traffic accidents. Clinical Neurophysiology 118, 1906–1922 (2007)
Yeo, M.V.M., Shen, X., Li, K., Wilder-Smith, E.P.V.: Can SVM be used for automatic EEG detection of drowsiness during car driving? Safety Science 47, 115–124 (2009)
Shuyan, H., Gangtie, Z.: Driver drowsiness detection with eyelid related parameters by Support Vector Machine. Expert Systems with Applications 36, 7651–7658 (2009)
Patel, M., Lal, S.K., Kavanagh, D., Rossiter, P.: Applying neural network analysis on heart rate variability data to assess driver fatigue. Expert Systems with Applications 38(6), 7235–7242 (2011)
Haenschel, C., Baldeweg, T., Croft, R.J., Whittington, M., Gruzelier, J.: Gamma and beta frequency oscillations in response to novel auditory stimuli: a comparison of human electroencephalogram (EEG) data with in vitro models. Proceedings of the National Academy of Sciences 97(13), 7645–7650 (2000)
Giraud, A.L., Poeppel, D.: Cortical oscillations and speech processing: emerging computational principles and operations. Nature Neuroscience 15(4), 511–517 (2012)
Price, K.V., Storn, R.M., Lampinen, J.A.: Differential Evolution: A Practical Approach to Global Optimization. Springer (2005)
Rechtschaffen, A., Kales, A.: A manual of standardized terminology, technique and scoring system for sleep stages of human sleep. Los Angeles Brain Information Service. Brain Information Institute, UCLA (1968)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Al-Ani, A., Van Dun, B., Dillon, H., Rabie, A. (2012). Analysis of Alertness Status of Subjects Undergoing the Cortical Auditory Evoked Potential Hearing Test. In: Huang, T., Zeng, Z., Li, C., Leung, C.S. (eds) Neural Information Processing. ICONIP 2012. Lecture Notes in Computer Science, vol 7663. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34475-6_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-34475-6_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-34474-9
Online ISBN: 978-3-642-34475-6
eBook Packages: Computer ScienceComputer Science (R0)