EEG Signal for Diagnosing Diseases using Machine Learning

Authors(3) :-Aswathy K J, Swathi Anil, Prof. Elizebath Issac

Alzheimer's disease is a chronic neurodegenerative disease that usually starts slowly and worsens over time. Alzheimer’s is an irreversible, progressive brain disorder that slowly destroys memory and thinking skills, and eventually the ability to carry out the simplest tasks. In most people, symptom first appears in their mid-60s. Studies have reported that electroencephalogram (EEG) signals of Alzheimer’s disease patients usually have less synchronization when compare to healthy people. Changes in EEG signals start at early stage but, clinically, these changes are not easily detected. Early detection of Alzheimer’s can have treatments with more positive outcomes. The aim of this paper is to classify Alzheimer’s disease patients using EEG signal processing in order to support medical doctors in the right diagnosis formulation. The proposed system consists of mainly five steps: Signal Acquisition, Pre-processing, Feature Extraction, Feature Selection, and Classification. The Signal Acquisition makes use of EEG dataset, Band-pass-filtering is used in the pre-processing stage to get artifact free signal. The necessary features are then extracted from EEG signals using Wavelet Transform and they are subjected to Principal Component Analysis (PCA) for feature selection. The classification is done using Block Based Neural Network (BBNN). Based on the changes in EEG the structure and internal configuration of BBNN are modified.

Authors and Affiliations

Aswathy K J
Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India
Swathi Anil
Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India
Prof. Elizebath Issac
Department of Computer Science and Engineering, Mar Athanasius College of Engineering, Kothamangalam, India

Alzheimer's disease (AD),EEG, Principal Component Analysis, BlockBased Neural Network.

  1. H. Berger, “On the Electroencephalogram of Man", Electroencephalography and Clinical Neurophysiology Suppl., vol 28, pp.37-73, 1969.
  2. H. Adeli, S. Ghosh-Dastidar, and N. Dadmehr, “A wavelet-chaos methodology for analysis of EEGs and EEG sub-bands to detect Alzheimer and epilepsy", IEEE Trans. Biomed. Eng., vol. 54, no. 2, pp. 205-211, Feb. 2007.
  3. H. A. Jasper, “The ten-twenty system of the International Federation", Electroencepholography and Clinical Neurophysiology, vol. 10, pp. 371-375, 1958.
  4. M. Steriade, D. A. McCormick, and T. J. Sejnowski, “Thalamocortical oscillations in the sleeping and aroused brain", Science, vol. 262, pp.679-685, 1993.
  5. J. B. Ochoa, “EEG Signal Classification for Brain Computer Interface Applications ECOLE POLYTECHNIQUE FEDERALE DE LAUSANNE", [Online] Available:
  6. S. Sanei, and J. A. Chambers, “Brain Rhythms", in EEG Signal Processing. New York: Wiley, 2007, pp. 10-12
  7. Lee,  B.;  Tarng,  Y.  S.  "Application  of the  discrete  wavelet  transform  to  the monitoring of tool failure in end milling using the    spindle motor current". International Journal of Advanced Manufacturing Technology,  1999, pp. 238–243
  8. E. Baar, M.Schrmann, C. Baar-Eroglu, and S. Karakas, “Alpha oscillations in brain functioning: an integrative theory", International Journal of Psychophysiology, 26(1-3), pp.5-29, 1997.
  9. Yinxia Liu, Weidong Zhou, Qi Yuan, and Shuangshuang Chen, “Automatic Alzheimer Detection Using Wavelet Transform and SVM in Long-Term Intracranial EEG”, IEEE Transactions On Neural Systems And Rehabilitation Engineering, Vol. 20, No. 6, November 2012.
  10. Sang Woo Moon and SeongGon Kong, “Block Based Neural Networks”, IEEE Transactions On Neural Networks, Vol.12, No.2, March 2001, pp 307 – 317.
  11. Wei Jiang and SeongGon Kong, “A Least Squares Learning for Block Based Neural Networks”, Advances in Neural Netwoks, Vol.14 (SI), 2007, pp 242 –247.
  12. A. Varsavsky, I., Mareels and M. Cook, Alzheimer and the EEG, Boca Raton: CRC Press, 2011.

Publication Details

Published in : Volume 3 | Issue 7 | September 2017
Date of Publication : 2017-12-31
License:  This work is licensed under a Creative Commons Attribution 4.0 International License.
Page(s) : 07-12
Manuscript Number : IJSRSET3702
Publisher : Technoscience Academy

Print ISSN : 2395-1990, Online ISSN : 2394-4099

Cite This Article :

Aswathy K J, Swathi Anil, Prof. Elizebath Issac, " EEG Signal for Diagnosing Diseases using Machine Learning, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 3, Issue 7, pp.07-12, September.2017

Follow Us

Contact Us