Predicting heat-stressed EEG spectra by self-organising feature map and learning vector quantizers——SOFM and LVQ based stress prediction
Prabhat Kumar Upadhyay, Rakesh Kumar Sinha, Bhuwan Mohan Karan
DOI: 10.4236/jbise.2010.35074   PDF    HTML     4,643 Downloads   8,477 Views   Citations


Self-Organising Feature Map (SOFM) along with learning vector quantizers (LVQ) have been designed to identify the alterations in brain electrical potentials due to exposure to high environmental heat in rats. Three groups of rats were considered—acute heat stressed, chronic heat stressed and control groups. After long EEG recordings following heat exposure, EEG data representing three different vigilance states such as slow wave sleep (SWS), rapid eye movement (REM) sleep and AWAKE were visually selected and further subdivided into 2 seconds long epoch. In order to evaluate the performance of artificial neural network (ANN) in recognizing chronic and acute effects of heat stress with respect to the control subjects, unsupervised learning algorithm was applied on EEG data. Mean performance of SOFM with quadratic taper function was found to be better (chronic-92.6%, acute-93.2%) over the other two tapers. The effect of LVQ after the initial SOFM training seems explicit giving rise to considerable improvements in performance in terms of selectivity and sensitivity. The percentage increase in selectivity with uniform taper function is maximum for chronic and its control group (4.01%) and minimum for acute group (1.29%) whereas, with Gaussian it is almost identical (chronic-2.57%, acute-2.03%, control- 2.33%). Quadratic taper function gives rise to an increase of 2.41% for chronic, 1.96% for acute and 2.91% for control patterns.

Share and Cite:

Upadhyay, P. , Sinha, R. and Karan, B. (2010) Predicting heat-stressed EEG spectra by self-organising feature map and learning vector quantizers——SOFM and LVQ based stress prediction. Journal of Biomedical Science and Engineering, 3, 529-537. doi: 10.4236/jbise.2010.35074.

Conflicts of Interest

The authors declare no conflicts of interest.


[1] Sinha, R.K. (2007) Study of changes in some pathophysiological stress arkers in different age groups of an animal model of acute and chronic heat stress. Iranian Biomedical Journal, 11(2), 101-111.
[2] Claude, R., Cristian, G. and Limoge, A. (1998) Review of neural network application in sleep research. Journal of Neuroscience methods, 79(2), 187-193.
[3] Morstyn, R., Duffy, F.H. and McCarley, R.W. (1983) Altered topography of EEG spectral content in schizophrenia. Electroencephalography and Clinical Neurophysiology, 56(4), 263-271.
[4] Lin, S.L., Tsai, Y.J. and Liou, C.Y. (1993) Conscious mental tasks and their EEG signal. Medical and Biological Engineering and Computing, 31(4), 421-426.
[5] Sarbadhikari, S.N., Dey, S. and Ray, A.K. (1996) Chronic exercise alters EEG power spectra in an animal model of depression. Indian Journal of Physiology and Pharmacology, 40(1), 47-57.
[6] Cesarelli, M., Clemente, F. and Bracale, M. (1990) A flexible FFT algorithm for processing biomedical signals using a PC. Journal of Biomedical Engineering, 12(6), 527-530.
[7] Sinha, R.K. (2003) Artificial Neural Network detects changes in electro-encephalogram power spectrum of different sleep-wakes in an animal model of heat stress. Medical and Biological Engineering and Computing, 41(5), 595-600.
[8] Veselis, R.A., Reinsel, R., Sommer, S. and Carlon, G. (1991) Use of neural network analysis to classify electroencephalographic patterns against depth of midazolam sedation in intensive care unit patients. Journal of Clinical Monitoring, 7(3), 259-267.
[9] Doering, A., Galicki, M., Witte, H. and Krajeca, V. (1995) Structure optimization of neural networks with A*-algorithm application in EEG pattern analysis. Medical Information for Patients, 8(1), 814-817.
[10] Witte, H., Doering, A., Galicki, M., Dorschel, J., Krajeca, V. and Eiselt, M. (1995) Application of optimized pattern recognition units in EEG analysis: Common optimization of preprocessing and weights of neural networks as well as structure optimization. Medi- cal Information for Patients, 8(1), 833-837.
[11] Sinha, R.K., Aggarwal, Y. and Das, B.N. (2007) Backpropagation artificial neural network classifier to detect changes in heart sound due to mitral valve regurgitation. Journal of Medical Systems, 31(3), 205- 209.
[12] Jansen, B.H. (1990) Artificial neural nets for K- Complex detection. IEEE Engineering in Medicine and Biology Magazine, 9(3), 50-52.
[13] Bankman, I.N., Sigillito, V.G., Wise, R.A. and Smith, P.L. (1992) Feature-based detection of the K-complex wave in the human electroencephalogram using neural networks. IEEE Transactions on Biomedical Engineering, 39(12), 1305-1310.
[14] Wu, F.Y., Slater, J.D., Honing, L.S. and Ramsay, R.E. (1993) A neural network design for event-related potential diagnosis. Computers in Biology and Medicine, 23(33), 251-264.
[15] Gupta, L., Molfese, D.L. and Tammana, R. (1995) An artificial neural network approach to ERP classification. Brain and Cognition, 27(3), 311-330.
[16] Bruha, I. and Madhvan, G.P. (1989) Need for a knowledge based subsystem in evoked potential neural net recognition system. Proceedings of the 11th Annual International Conference on IEEE-EMBS, 6, 2042-2043.
[17] Holdaway, R.M., White, M.W. and Marmarou, A. (1990) Classification of somatosensory evoked potentials recorded from patients with severe head injuries. IEEE Engineering in Medicine and Biology Magazine, 9(3), 43-49.
[18] James, C.J., Jones, R.D., Bones, P.J. and Carrol, G.J. (1996) The self-organizing feature map in the detection of epileptiform transients in the EEG. Proceedings of the 18th international Conference of the IEEE Engineering in Medicine and Biology Society, Amsterdam, 1996, 913-914.
[19] Adeli, H., Zhou, Z. and Dadmehr, N. (2003) Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123(1), 69-87.
[20] Kohonen, T. (1990) The self-organizing map. Proceedings of the IEEE, 73, 1464-1480.
[21] Kohonen, T. (1989) Self organization and associative memory. 3rd Edition, Springer-Verlag, Berlin.
[22] Kohonen, T. (1988) Learning vector quantization. Neural Networks, 1, 303.
[23] Dubois, M., Sato, S., Lees, D.E., Bull, J.M., Smith, R., White, B.G., Moore, H. and Macnamara, T. (1980) Electroencephalographic changes during whole body hyperthermia in humans. Electroencephalography and Clinical Neurophysiology, 50(5-6), 486-495.
[24] Sarbadhikari, S.N. (1995) A neural network confirms that physical exercise reverses EEG changes in depressed rats. Medical Engineering & Physics, 17(8), 579-582.
[25] Jandó, G., Siegel, R.M., Horvàth, Z. and Buzsàki, G. (1993) Pattern recognition of the electroencephalogram by artificial neural networks. Electroencephalography and Clinical Neurophysiology, 86(2), 100-109.

Copyright © 2024 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.