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A power spectrum based backpropagation artificial neural network model for classification of sleep-wake stages in rats

Sinha, Mr Rakesh Kumar and Agrawal, Mr Navin Kumar and Ray, Dr Amit Kumar (2003) A power spectrum based backpropagation artificial neural network model for classification of sleep-wake stages in rats. [Journal (On-line/Unpaginated)]

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Abstract

Three layered feed-forward backpropagation artificial neural network architecture is designed to classify sleep-wake stages in rats. Continuous three channel polygraphic signals such as electroencephalogram, electrooculogram and electromyogram were recorded from conscious rats for eight hours during day time. Signals were also stored in computer hard disk with the help of analog to digital converter and its compatible data acquisition software. The power spectra (in dB scale) of the digitized signals in three sleep-wake stages were calculated. Selected power spectrum data of all three simultaneously recorded polygraphic signals were used for training the network and to classify slow wave sleep, rapid eye movement sleep and awake stages. The ANN architecture used in present study shows a very good agreement with manual sleep stage scoring with an average of 94.83% for all the 1200 samples tested from SWS, REM and AWA stages. The high performance observed with the system based on ANN highlights the need of this computational tool into the field of sleep research.

Item Type:Journal (On-line/Unpaginated)
Keywords:Artificial neural network, Power spectrum, Sleep-wake states
Subjects:JOURNALS > Online Journal of Health and Allied Sciences
ID Code:3227
Deposited By: Kakkilaya Bevinje, Dr. Srinivas
Deposited On:17 Oct 2003
Last Modified:11 Mar 2011 08:55

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

1. Guyton AC. Human physiology and mechanisms of disease. 5th ed., New Delhi: W B Saunders Co.; 1976. p. 734-42.

2. Gath I, Bar On E. Computerised method for scoring of polygraphic sleep recordings. Compt Prog Biomed. 1980; 11(3): 217-23.

3. Gath I, Bar On E. Classical sleep stages and the spectral contents of EEG signals. Int J Neurosci. 1983; 22 (1-2): 147-55.

4. Gath I, Bar On E, Rogowski Z, et al. Automatic scoring of polygraphic sleep recordings: Midazolam in insomniacs. Br J Pharmacol. 1983; 16 Suppl 1: 89S-96S.

5. Stanus E, Lacroix B, Kerkhofs M, et al. Automated sleep scoring: a comparitive reliability of two algorithms. Electroenceph Clin Neurophy. 1987; 66: 448-56.

6. Clark FM, Radulovacki M. An inexpensive sleep-wake state analyzer for the rat. Physiol Behav. 1988; 43: 681-83.

7. Gevins AS, Stone RK, Ragsdale SD. Differentiating the effects of three benzodizepines on non REM sleep EEG spectra. A neural network pattern classification analysis. Neuropsychobiology. 1988; 19(2): 108-15.

8. Mamelak A, Quattrochi JJ, Hobson JA. A microcomputer based system for automated EEG collection and scoring of behavioural state in cats. Brain Res Bull. 1988; 21: 843-49.

9. Ferri R, Ferri P, Colognola RM, et al. Comparison between the results of an automatic and visual scoring of sleep EEG records. Sleep. 1989; 12: 354-62.

10. Agarwal R, Gotman J. Computer-assisted sleep staging. IEEE Trans BME. 2001; 48(12): 1412-23.

11. Mamelak A, Quattrochi JJ, Hobson, JA. Automatic staging of sleep in cats using neural networks. Electroenceph Clin Neurophy. 1991; 79: 52-61.

12. Shimada T, Shiina T. Detection of characteristic waves of sleep EEG by neural network analysis. IEEE Trans BME. 2000; 47(3): 369-79.

13. Rumelhart DE, McClelland JL. On learning the past tense of English verbs. In McClelland JL, Rumelhart DE eds. Parellel distributed processing: Explorations in the microstructure of cognition. Vol-II, M I T press, Cambridge M A; 1986. p. 216-68.

14. Rumelhart DE, Hinton GE, Williams RJ. Learning representations by back-propagating errors. Nature. 1986; 323: 533-36.

15. O’Boyle DJ, Choi EWK, Conroy G, et al. Learned classification of EEG power spectra using a neural network. J Physiol, Proc Physiol Society, Shaffield Meeting; 19-20 April 1991. p. 438: 345.

16. Sarbadhikari SN, Dey S, Ray AK. Chronic exercise alters EEG power spectra in an animal model of depression. Ind J Physiol Pharmacol. 1996; 40(1): 47-57.

17. Sarbadhikari SN. Neural network aided analysis of electrophysiological signals from brain of an animal model of depression subjected to chronic physical exercise. Ph D Thesis, Banaras Hindu University, India 1995a; 80.

18. Sirne RO, Isaacson SI, D’Attellis CE. A data-reduction process for long-term EEGs. IEEE Engg Med & Biol. Jan/Feb 1999; 56-61.

19. Rao V, Rao H. C++ Neural Networks and Fuzzy Logic. First Edition. New Delhi: BPB Publications; 1996. p. 123-76.

20. Hassoun HM. Fundamentals of artificial neural networks. New Delhi: Printice-Hall of India Private Limited; 1998. p. 35-56.

21. Webber WRS, Lesser RP, Richardson RT, et al. An approach to seizure detection using an artificial neural network. Electroenceph Clin Neurophy. 1996; 98: 250-72.

22. Pfurtscheller G, Flotzinger D, Matuschik K. Sleep classification in infants based on artificial neural networks. Biomed. Tech. Berl. 1992; 37(6): 122-30.

23. Schaltenbrand N, Lengelle R, Macher JP. Neural network model: application to automatic analysis of human sleep. Comput. Biomed. Res. 1993; 26(2): 157-71.

24. Schaltenbrand N, Lengelle R, Toussaint M, et al. Sleep stage scoring using neural network model: comparison between visual and automatic analysis in normal subjects and patients. Sleep. 1996; 19(1): 25-35.

25. Grozinger M, Roschke, J. Recognition of rapid-eye-movement sleep from single channel EEG data by artificial neural network: a study in depressive patients with and without amitriptyline treatment, Neuropsychobiology. 1996; 33(3): 155-59.

26. Jervis BW, Coelho M, Morgan GW. Spectral analysis of EEG responses. Med & Biol Engg & Comp. 1989; 27: 230-38.

27. Kulkarni PK, Kumar V, Verma HK. Diagnostic acceptability of FFT-based ECG data compression. J Med Engg & Tech. 1997; 21(5): 185-89.

28. Al-Nashash HAM. A dynamic Fourier series for the compression of ECG using FFT and adaptive coefficient estimate. Med Engg & Phy. 1995; 17(3): 197-203.

29. Jandó G, Seigel RM, Horváth Z, Buzsáki G. Pattern recognition of the electroencephalogram by artificial neural networks. Electroenceph Clin Neurophy. 1993; 86: 100-9.

30. Sarbadhikari SN, Ray AK. Identifying EEG power spectra of depressed rats using a neural network. In Reddy DC, ed. Recent Advances in Biomedical Engineering. New Delhi: Tata McGrow-Hill; 1994. p. 76-79.

31. Sarbadhikari SN. A Neural network confirms that physical exercise reverses EEG changes in depressed rats. Med Engg & Phy. 1995b; 17(8): 579-82.

32. Sinha RK, Backpropagation Artificial neural network to detect hyperthermic seizures in rats. Online J. Health Allied Scs. 2002; 4(1).

33. Villiers JD, Barnard E. Backpropagation neural nets with one and two hidden layers. IEEE Trans Neural Network. 1992; 4: 136-41.

34. Chen JDZ, Lin Z, Wu Q, et al. Non-invasive identification of gastric contractions from surface electrogastrogram using backpropagation neural networks. Med Engg & Phy. 1995; 17(3): 219-25.

35. Zurada JM. Introduction to artificial neural network systems. St. Paul, MN: West Publishing Company; 1997. p. 163-250.

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