Statistical Wavelet Features, PCA, MLPNN, SVM and K-NN Based Approach for the Classification of EEG Physiological Signal
International Journal of Industrial and Manufacturing Systems Engineering
Volume 2, Issue 5, September 2017, Pages: 57-65
Received: Sep. 9, 2017; Accepted: Sep. 25, 2017; Published: Nov. 5, 2017
Views 2016      Downloads 76
Manisha Chandani, Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India
Arun Kumar, Department of Electronics & Telecommunication, Bhilai Institute of Technology, Durg, India
Article Tools
Follow on us
Brain is the most complex organ amongst all the systems in human body. The study of the electrical signals produced by neural activities of human brain is called Electroencephalogram. Electroencephalogram (EEG) is a technique which is used to identify the neurological disorder of brain. Epilepsy is one of the most common neurological disorders of brain. Epilepsy needs to be detected efficiently using required EEG feature extraction such as: mean, standard deviation, median, entropy, kurtosis and skewness etc. The framework of proposed technique is an efficient EEG signal classification approach. The proposed approach is used to classify the EEG signal into two classes: epileptic seizure or not. Extraction of the features by applying Discrete Wavelet Transform (DWT) in order to decompose the EEG signals into sub-bands. These features, extracted from details and approximation coefficients of DWT sub-bands, are used as input to Principal Component Analysis (PCA). The classification is based on reducing the feature dimension using PCA and deriving the Support Vector Machine (SVM), neural network analysis (NNA) and k-nearest neighbour (K-NN). In classification of normal and epileptic, results obtained exhibited an accuracy of 100% by applying NNA and k-NN. It has been found that the computation time of NNA classifier is lesser than SVM and k-NN to provide 100% accuracy. So, the detection of an epileptic seizure based on DWT statistical features using NNA classifiers is more suitable in real time for a reliable, automatic epileptic seizure detection system to enhance the patient's care and the quality of life.
Electroencephalogram (EEGs), Discrete Wavelet Transform (DWT), Principal Component Analysis (PCA), Neural Network Analysis (NNAs), K-Nearest Neighbour (k-NN), Support Vector Machine (SVM), Epileptic, Seizure
To cite this article
Manisha Chandani, Arun Kumar, Statistical Wavelet Features, PCA, MLPNN, SVM and K-NN Based Approach for the Classification of EEG Physiological Signal, International Journal of Industrial and Manufacturing Systems Engineering. Vol. 2, No. 5, 2017, pp. 57-65. doi: 10.11648/j.ijimse.20170205.12
Copyright © 2017 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License ( which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Saeid Sanei and J. A. Chambers EEG Signal Processing. John Wiley & Sons, 2007.
A. Massimo. “In Memoriam Pierre Gloor 1923–2003): an appreciation”. Epilepsia, vol.-45(7), page-882, 2004.
M. A. B. Brazier. “A History of the Electrical Activity of the Brain”. The First Half-Century, Macmillan, New York, 1961.
M. D. Alessandro, R. Esteller& G. Vachtsevanos. A. Hinson, A. Echauz, and B. Litt. "Epileptic seizure prediction using hybrid feature selection over multiple intracranial EEG electrode contacts: A report of four patients". IEEE Transactions on Biomedical Engineering. vol. 50 (5), pp.-603–615, 2003.
B. P. Marchant. “Time–frequency analysis for biosystem engineering”. Biosystems Engineering. vol.-85 (3), pp.-261–281, 2003.
A. Subasi. "EEG signal classification using wavelet function extraction and a mixture of expert model', Expert System with Application, 32, 1084-1093, 2007.
A. Subasi, and M. Ismail Gursoy. “EEG signal classification using PCA, ICA, LDA and support vector machines”. Expert Systems with Applications. vol. 37, pp.-8659–8666, 2010.
A. Subas. “Epileptic seizure detection using dynamic wavelet network”. Expert Systems with Applications.. vol.-29, pp.-343–355, 2005.
K. Fu, J. Qu, Y. Chai, & Y. Dong. “Classification of seizure based on the time-frequency image of EEG signals using HHT and SVM”. Biomedical Signal Processingand Control. vol.-13, pp.-15–22, 2014.
R. G. Andrezejak, K. Lehnertz & F. Morman. Indication of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. Ed-64 (6) – 061907, 2001.
Durka P. J. Adaptive time-frequency parametrization of epileptic spikes. Physical Review E; 69: 051914, 2004.
Kumari Pinki & Abhishek Vaish. Brainwave based user identification system: A pilot study in robotics environment. Robotics and Autonomous Systems 65, pp. 15-23, 2015.
A. Subasi. “EEG signal classification using wavelet function extraction and a mixture of expert model”, Expert System with Application, 32, pp.1084-1093, 2007.
C. S. Burrus, R. A. Gopinath, & H. Guo (1998). Introduction to wavelets and wavelet transforms: A primer. Prentice-Hall, Upper Saddle River, NJ.
Mandeep Singh & Sunpreet Kaur. Epilepsy, “Frequency Band Separation for Epilepsy Detection Using EEG”, International Journal of Information Technology & Knowledge Management, Vol 6, No.1, 2012.
Claude Roberta, Jean-Franc¸ ois Gaudyb & Aime´ Limogea. “Electroencephalogram processing using neural networks”, Clinical Neurophysiology 113, pp.694–701, 2002.
S. Theodoridis & K Koutroumbas. Pattern Recognition. 4th Ed Elsevier - Academic Press, 2009.
P. S. Sastry. “An introduction to Support Vector Machines”. Chapter in J. C. Misra (Ed), computing and information sciences: Recent Trends. Narosa Publishing House, New Delhi, 2003.
H. Peng, F. Long, & C. Ding. “Feature selection based on mutual information: criteria of max-dependency, maxrelevance, and min-redundancy”. IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, No. 8, pp. 1226- 1238, 2005.
Alireza Baratloo, Mostafa Hosseini, Ahmmed Negida & Gehad El Ashal. “Simple Definition and Calculation of Accuracy, Sensitivity and Specificity”. Volume 4 No. 2, pp. 48–49, 2015.
Semwal, Vijay Bhaskar, Manish Raj, and Gora Chand Nandi. “Biometric gait identification based on a multilayer perceptron.” Robotics and Autonomous Systems 65 pp. 65-75, 2015.
Khan Y. U, Farooq O & Sharma P. “Automatic detection of seizure onset in pediatric EEG”. International Joural of Embeded Systems and Applications. Vol. 2 pp. 81-89, 2012.
Meier R, Dittrich H, Schulze-Bonhage A & Aertsen A. “Detecting epileptic seizures in long-term human EEG: a new approach to automatic online and real-time detection and classification of polymorphic seizure patterns”. Journal of Clinical Neurophysiology Vol25, pp. 119-131, 2008.
Stein A. G, Eder H. G, Blum D. E, Drachev A & Fisher R. S. “An automated drug delivery system for focal epilepsy”. Epilepsy research Vol.39, pp. 103-114, 2000.
Science Publishing Group
1 Rockefeller Plaza,
10th and 11th Floors,
New York, NY 10020
Tel: (001)347-983-5186