Open Science Research Excellence

Open Science Index

Commenced in January 2007 Frequency: Monthly Edition: International Publications Count: 29705


Select areas to restrict search in scientific publication database:
15785
Improved Automated Classification of Alcoholics and Non-alcoholics
Abstract:
In this paper, several improvements are proposed to previous work of automated classification of alcoholics and nonalcoholics. In the previous paper, multiplayer-perceptron neural network classifying energy of gamma band Visual Evoked Potential (VEP) signals gave the best classification performance using 800 VEP signals from 10 alcoholics and 10 non-alcoholics. Here, the dataset is extended to include 3560 VEP signals from 102 subjects: 62 alcoholics and 40 non-alcoholics. Three modifications are introduced to improve the classification performance: i) increasing the gamma band spectral range by increasing the pass-band width of the used filter ii) the use of Multiple Signal Classification algorithm to obtain the power of the dominant frequency in gamma band VEP signals as features and iii) the use of the simple but effective knearest neighbour classifier. To validate that these two modifications do give improved performance, a 10-fold cross validation classification (CVC) scheme is used. Repeat experiments of the previously used methodology for the extended dataset are performed here and improvement from 94.49% to 98.71% in maximum averaged CVC accuracy is obtained using the modifications. This latest results show that VEP based classification of alcoholics is worth exploring further for system development.
Digital Object Identifier (DOI):

References:

[1] X. L. Zhang, H. Begleiter, B. Porjesz, and A. Litke, "Electrophysiological evidence of memory impairment in alcoholic patients," Biological Psychiatry, vol.42, pp. 1157-1171, 1997.
[2] E. Basar, Memory and Brain Dynamics: Oscillations Integrating Attention, Perception, Learning, and Memory, CRC Press, Boca Raton, 2004.
[3] K. E. Misulis, Spehlmann-s Evoked Potential Primer: Visual, Auditory and Somatosensory Evoked Potentials in Clinical Diagnosis, Butterworth-Heinemann, 1994.
[4] R. Palaniappan, P. Raveendran, and S. Omatu, "VEP optimal channel selection using genetic algorithm for neural network classification of alcoholics," IEEE Transactions on Neural Network, vol. 13, issue 2, pp.486-491, 2002.
[5] H. Jasper, "The ten twenty electrode system of the international federation," Electroencephalographic and Clinical Neurophysiology, vol. 10, pp. 371-375, 1958.
[6] J. G. Snodgrass and M. Vanderwart, "A standardized set of 260 pictures: norms for name agreement, image agreement, familiarity, and visual complexity," Journal of Experimental Psychology: Human Learning and Memory, vol. 6, no. 2, pp. 174-215, 1980.
[7] A. Kriss, "Recording technique," in Evoked Potentials in Clinical Testing, Halliday, A.M.(ed), Churchill Livingstone, 1993.
[8] J. I. Aunon, C. D. McGillem, and D. G. Childers, "Signal processing in event potential research: averaging and modelling," CRC Crit. Rev. Bioeng., vol 5, pp. 323-367, 1981.
[9] S. K. Mitra, Digital Signal Processing, 3rd ed., McGraw-Hill International Edition, 2006.
[10] G. E. P. Box, and G. M. Jenkins, Time Series Analysis: Forecasting and Control, Holden Day, San Francisco, 1976.
[11] M. Akay, Detection and Estimation Methods for Biomedical Signals, Academic Press, 1996.
[12] R. Palaniappan, "Discrimination of alcoholic subjects using second order autoregressive modelling of brain signals evoked during visual stimulus perception," 5th World Enformatika Conference, Prague, Czech Republic, August 26-28, 2005 (accepted for presentation).
[13] O. Caspary, M. Tomczak, N. Di Renzo, M. Mouze-Amady and D. Henry, "Enhanced high resolution spectral analysis of sleep spindles," Proceedings of the 16th Annual International Conference of the IEEE EMBS, vol.2, pp. 1232 - 1233, 1994.
[14] C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
[15] M. Riedmiller, and H. Braun, "A direct adaptive method for faster backpropagation learning: the RPROP algorithm," Proceedings of the IEEE International Conference on Neural Networks, pp. 586-591, 1993.
Vol:13 No:06 2019Vol:13 No:05 2019Vol:13 No:04 2019Vol:13 No:03 2019Vol:13 No:02 2019Vol:13 No:01 2019
Vol:12 No:12 2018Vol:12 No:11 2018Vol:12 No:10 2018Vol:12 No:09 2018Vol:12 No:08 2018Vol:12 No:07 2018Vol:12 No:06 2018Vol:12 No:05 2018Vol:12 No:04 2018Vol:12 No:03 2018Vol:12 No:02 2018Vol:12 No:01 2018
Vol:11 No:12 2017Vol:11 No:11 2017Vol:11 No:10 2017Vol:11 No:09 2017Vol:11 No:08 2017Vol:11 No:07 2017Vol:11 No:06 2017Vol:11 No:05 2017Vol:11 No:04 2017Vol:11 No:03 2017Vol:11 No:02 2017Vol:11 No:01 2017
Vol:10 No:12 2016Vol:10 No:11 2016Vol:10 No:10 2016Vol:10 No:09 2016Vol:10 No:08 2016Vol:10 No:07 2016Vol:10 No:06 2016Vol:10 No:05 2016Vol:10 No:04 2016Vol:10 No:03 2016Vol:10 No:02 2016Vol:10 No:01 2016
Vol:9 No:12 2015Vol:9 No:11 2015Vol:9 No:10 2015Vol:9 No:09 2015Vol:9 No:08 2015Vol:9 No:07 2015Vol:9 No:06 2015Vol:9 No:05 2015Vol:9 No:04 2015Vol:9 No:03 2015Vol:9 No:02 2015Vol:9 No:01 2015
Vol:8 No:12 2014Vol:8 No:11 2014Vol:8 No:10 2014Vol:8 No:09 2014Vol:8 No:08 2014Vol:8 No:07 2014Vol:8 No:06 2014Vol:8 No:05 2014Vol:8 No:04 2014Vol:8 No:03 2014Vol:8 No:02 2014Vol:8 No:01 2014
Vol:7 No:12 2013Vol:7 No:11 2013Vol:7 No:10 2013Vol:7 No:09 2013Vol:7 No:08 2013Vol:7 No:07 2013Vol:7 No:06 2013Vol:7 No:05 2013Vol:7 No:04 2013Vol:7 No:03 2013Vol:7 No:02 2013Vol:7 No:01 2013
Vol:6 No:12 2012Vol:6 No:11 2012Vol:6 No:10 2012Vol:6 No:09 2012Vol:6 No:08 2012Vol:6 No:07 2012Vol:6 No:06 2012Vol:6 No:05 2012Vol:6 No:04 2012Vol:6 No:03 2012Vol:6 No:02 2012Vol:6 No:01 2012
Vol:5 No:12 2011Vol:5 No:11 2011Vol:5 No:10 2011Vol:5 No:09 2011Vol:5 No:08 2011Vol:5 No:07 2011Vol:5 No:06 2011Vol:5 No:05 2011Vol:5 No:04 2011Vol:5 No:03 2011Vol:5 No:02 2011Vol:5 No:01 2011
Vol:4 No:12 2010Vol:4 No:11 2010Vol:4 No:10 2010Vol:4 No:09 2010Vol:4 No:08 2010Vol:4 No:07 2010Vol:4 No:06 2010Vol:4 No:05 2010Vol:4 No:04 2010Vol:4 No:03 2010Vol:4 No:02 2010Vol:4 No:01 2010
Vol:3 No:12 2009Vol:3 No:11 2009Vol:3 No:10 2009Vol:3 No:09 2009Vol:3 No:08 2009Vol:3 No:07 2009Vol:3 No:06 2009Vol:3 No:05 2009Vol:3 No:04 2009Vol:3 No:03 2009Vol:3 No:02 2009Vol:3 No:01 2009
Vol:2 No:12 2008Vol:2 No:11 2008Vol:2 No:10 2008Vol:2 No:09 2008Vol:2 No:08 2008Vol:2 No:07 2008Vol:2 No:06 2008Vol:2 No:05 2008Vol:2 No:04 2008Vol:2 No:03 2008Vol:2 No:02 2008Vol:2 No:01 2008
Vol:1 No:12 2007Vol:1 No:11 2007Vol:1 No:10 2007Vol:1 No:09 2007Vol:1 No:08 2007Vol:1 No:07 2007Vol:1 No:06 2007Vol:1 No:05 2007Vol:1 No:04 2007Vol:1 No:03 2007Vol:1 No:02 2007Vol:1 No:01 2007