Detection and analysis of the effects of heat stress on EEG using wavelet transform ——EEG analysis under heat stress
ABSTRACT
Continuous wavelet transform (CWT) method has been applied to capture localized time-frequency information of rat electroencephalogram (EEG) in different vigilance states and analyze alterations in transients during awake, slow wave sleep (SWS), and rapid eye movement (REM) sleep stages due to exposure to high environmental heat. Rats were divided in three group (i) acute heat stress-subjected to a single exposure for four hours in the Biological Oxygen Demand (BOD) incubator at 38?C; (ii) chronic heat stress-exposed for 21 days daily for one hour in the incubator at 38?C, and (iii) handling control groups. After two hours long EEG recordings from young healthy rats, EEG data representing three sleep states was visually selected and further subdivided into 2 seconds long epoch. Powers of wavelet spectra corresponding to delta, theta, alpha, and beta bands at all scales and locations were computed and variation in their states investigated. The wavelet analysis of EEG signals following exposure to high environmental heat revealed that powers of subband frequencies vary with time unlike Fourier technique. Changes in higher frequency components (beta) were significant in all sleep-wake states following both acute and chronic heat stress conditions. Percentage power of different components of the four bands was always found to be varying at different intervals of time in the same signal of analysis.
Continuous wavelet transform (CWT) method has been applied to capture localized time-frequency information of rat electroencephalogram (EEG) in different vigilance states and analyze alterations in transients during awake, slow wave sleep (SWS), and rapid eye movement (REM) sleep stages due to exposure to high environmental heat. Rats were divided in three group (i) acute heat stress-subjected to a single exposure for four hours in the Biological Oxygen Demand (BOD) incubator at 38?C; (ii) chronic heat stress-exposed for 21 days daily for one hour in the incubator at 38?C, and (iii) handling control groups. After two hours long EEG recordings from young healthy rats, EEG data representing three sleep states was visually selected and further subdivided into 2 seconds long epoch. Powers of wavelet spectra corresponding to delta, theta, alpha, and beta bands at all scales and locations were computed and variation in their states investigated. The wavelet analysis of EEG signals following exposure to high environmental heat revealed that powers of subband frequencies vary with time unlike Fourier technique. Changes in higher frequency components (beta) were significant in all sleep-wake states following both acute and chronic heat stress conditions. Percentage power of different components of the four bands was always found to be varying at different intervals of time in the same signal of analysis.
Cite this paper
nullUpadhyay, P. , Sinha, R. and Karan, B. (2010) Detection and analysis of the effects of heat stress on EEG using wavelet transform ——EEG analysis under heat stress. Journal of Biomedical Science and Engineering, 3, 405-414. doi: 10.4236/jbise.2010.34056.
nullUpadhyay, P. , Sinha, R. and Karan, B. (2010) Detection and analysis of the effects of heat stress on EEG using wavelet transform ——EEG analysis under heat stress. Journal of Biomedical Science and Engineering, 3, 405-414. doi: 10.4236/jbise.2010.34056.
References
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(1998) Pathophysiology of brain edema and cell changes following hyperthermic brain injury: Progress in Brain Research. Elsevier, Amsterdam, 115, 351-412. [34] Sinha, R.K. and Ray, A.K. (2004) An assessment of changes in open-field and elevated plus-maze behavior following heat stress in rats. Iranian Biomedical Journal, 8, 127-133.
[1] Sinha, R.K. (2004) Electro-encephalogram disturbances in different sleep-wake states following exposure to high environmental heat. Medical & Biological Engineering & Computing, 42, 282-287. [2] Sinha, R.K. (2007) Study of changes in some pathophysiological stress markers in different age groups of an animal model of acute and chronic heat stress. Iranian Biomedical Journal, 11, 101-111. [3] Guler, I., Kiymik, M.K., Akin, M. and Alkan, A. (2001) AR spectral analysis of EEG signals by using maximum likelihood estimation. Computers in Biology and Medicine, 31, 441-450. [4] Herrmann, C.S., Arnold, T., Visbeck, A., Hundemer, H.P. and Hopf, H.C. (2001) Adaptive frequency decomposition of EEG with subsequent expert system analysis. Computers in Biology and Medicine, 31, 407-427. [5] Peters, B.O., Pfurtscheller, G. and Flyvbjerg, H. (2001) Automatic differentiation of multichannel EEG analysis. IEEE transactions on Biomedical Engineering, 48, 111–116. [6] Vuckovick, A., Radivojevic, V., Chen A.C.N. and Popovic, D. (2002) Automatic recognition of alertness and drowsiness from EEG by an artificial neural network. Medical Engineering and Physics, 24, 349-360. [7] McKeon, M.J., Humphries, C., Achermann, P., Borbely, A.A. and Sejnowski, T.J. (1997) Anew method for detecting state changes in EEG: Exploratory application to sleep data. Journal of Sleep Research, 7, 48-56. [8] Dement, W.C. and Kleitman, N. (1957) Cyclic variations in EEG during sleep and their relation to eye movements, body motility and dreaming. Electroencephalography and Clinical Neurophysiology, 9, 673-690. [9] Jansen, B.H., Hasman, A. and Lenten, R. (1981) Piece- wise EEG analysis: An objective evaluation, International Journal of Bio-Medical Computing, 12, 17-27. [10] Al-Nashash, H.A.M. (1995) A dynamic Fourier series for the compression of ECG using FFT and adaptive coefficient estimate. Medical Engineering and Physics, 17, 197-203. [11] Sinha, R.K. (2003) Artificial neural network detects changes in electro-encephalogram power spectrum of different sleep-wake states in an animal model of heat stress. Medical & Biological Engineering & Computing, 41, 595-600. [12] Sarbadhikari, S.N., Dey, S., Ray, A.K. (1996) Chronic exercise alters EEG power spectra in an animal model of depression. Indian Journal of Physiology and Pharmacology, 40, 47-57. [13] Jung, T.P., Makeig, S., Stensmo, M. and Sejnowski, T.J. (1997) Estimating alertness from the EEG power spectrum. IEEE Transactions on Biomedical Engineering, 44, 60-69. [14] Subasi, A. (2005) Automatic recognition of alertness level from EEG by using neural network and wavelet coefficients. Expert Systems with Applications, 28, 701-711. [15] Adeli, H. et al. (2007) A wavelet-chaos methodology for analysis of EEGs and EEG Subbands to detect seizure and epilepsy. IEEE Transactions on Biomedical Engineering, 54(2), 205-211. [16] Adeli, H., Zhou, Z. and Dadmehr, N. (2003) Analysis of EEG records in an epileptic patient using wavelet transform. Journal of Neuroscience Methods, 123, 69-87. [17] Feng, Z. and Xu, Z. (2002) Analysis of rat electroencephalogram under slow wave sleep using wavelet transform. Proceedings of the Second Joint EMBS/BMES Conference Houston, TX, USA. [18] Daubechies, I. (1990) The Wavelet transform time-frequency localization and signal analysis. IEEE Transactions on Information Technology, 36(5), 961-1005. [19] Mallat, S. (1989) A theory for multiresolution signal decomposition: The wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(7), 674-693. [20] Meyer, Y. (1989) Orthonormal wavelets in Wavelets, Time-Frequency Methods and Phase Space (Lecture Notes on IPTI), J. M. Combes et al., Ed., New Work: Springer-Veriag. [21] Bianchi, A.M., Mainardi, L.T. and Cerutti, S. (2000) Time-frequency analysis of biomedical signals. Transactions of the Institute of Measurement and Control, 22, 321-336. [22] Kalayci, T. and Ozdamar, O. (1995) Wavelet preprocessing for automated neural network detection of EEG spikes. IEEE Engineering in Medicine and Biology Magazine, 14, 160-166. [23] Farge, M. (1992) Wavelet transforms and their applications to turbulence. Annual Review of Fluid Mechanics, 24, 395-457. [24] Sakkalis, V. et al. (2006) Significant EEG features involved in mathematical reasoning: Evidence from wavelet analysis. Brain Topography, 19(1/2), 53-60. [25] Shen, M., Sun, L. and Chan, F.H.Y. (2001) Method of extracting time-varying rhythms of electroencephalography via wavelet packet analysis. IEE Proceedings-Science, Measurement and Technology, 148. [26] Ting, W. et al. (2007) EEG feature extraction based on wavelet packet decomposition for brain computer interface. Meaurement. [27] Unser, M. and Aldroubi, A. (1996) A review of wavelets in biomedical applications. Proceedings of the IEEE, 84(4), 626-638. [28] Menon, M.K. and Dandiya, P.C. (1969) Behavioural and brain neurohormonal changes produced by acute heat stress in rats: Influence of psychopharmacological agents. European Journal of Pharmacology, 8, 284-291. [29] Sharma, H.S. (1982) Blood-brain barrier (BBB) in stress. Ph. D. Thesis, Zoology, Banaras Hindu University. [30] Dey, P.K. (1998) Modification of dopamine receptor agonist mediated behavioral responses in rats following exposure to chronic heat stress. Biomedicine, 18, 41-47. [31] Dey, P.K. (2000) Involvement of endogenous opiates in heat stress. Biomedicine, 20, 143-148. [32] Dubois, M., Sato, S., Lees, D.E., Bull, J.M., Smith, R., White, B.G., Moore, H. and Macnamara, T.E. (1980) Electroencephalographic changes during whole body hyperthermia in humans. Electroencephalography and Clinical Neurophysiology, 50, 486-495. [33] Sharma, H.S., Westman, J. and Nyberg, F. (1998) Pathophysiology of brain edema and cell changes following hyperthermic brain injury: Progress in Brain Research. Elsevier, Amsterdam, 115, 351-412. [34] Sinha, R.K. and Ray, A.K. (2004) An assessment of changes in open-field and elevated plus-maze behavior following heat stress in rats. Iranian Biomedical Journal, 8, 127-133.