A Channel Selection Method for Epileptic EEG Signals

Epilepsy is a disorder of the central nervous system in which a considerably large number of neurons at a certain instance of time show abnormal electrical activity. EEG or electroencephalogram signals plays a significant role in the diagnosis of epilepsy. Worldwide roughly 50 million people are affected by epilepsy that includes patients from all walks of life. In this paper we have studied the performance of artificial neural network (ANN) on EEG signals (CHB-MIT database) by applying principal component analysis (PCA) for selection of channels. The results reflect the performance of the neural network in different configuration. Out of the 23 channels considered, after using PCA, the highest accuracy of 86.7% is achieved with 18 channels. With the reduction of channels the accuracy decreased simultaneously. The study also brings forth the shortcomings as well as determines the area in this domain that holds prospective for future scope of work.

[1]  Tobias Loddenkemper,et al.  Seizure detection, seizure prediction, and closed-loop warning systems in epilepsy , 2014, Epilepsy & Behavior.

[2]  Dimitrios I. Fotiadis,et al.  EEG epileptic seizure detection using k-means clustering and marginal spectrum based on ensemble empirical mode decomposition , 2013, 13th IEEE International Conference on BioInformatics and BioEngineering.

[3]  Ajith Abraham,et al.  Artificial neural networks , 2005 .

[4]  Manolis Tsiknakis,et al.  Methods for Seizure Detection and Prediction: An Overview , 2014 .

[5]  R. Harner,et al.  Patient-Specific Early Seizure Detection From Scalp Electroencephalogram , 2010, Journal of clinical neurophysiology : official publication of the American Electroencephalographic Society.

[6]  Manoranjan Paul,et al.  Epileptic seizure detection by analyzing EEG signals using different transformation techniques , 2014, Neurocomputing.

[7]  Raúl Rojas,et al.  Neural Networks - A Systematic Introduction , 1996 .

[8]  P. Geethanjali,et al.  DWT Based Detection of Epileptic Seizure From EEG Signals Using Naive Bayes and k-NN Classifiers , 2016, IEEE Access.

[9]  J. Gotman,et al.  A system to detect the onset of epileptic seizures in scalp EEG , 2005, Clinical Neurophysiology.

[10]  A. Aarabi,et al.  A fuzzy rule-based system for epileptic seizure detection in intracranial EEG , 2009, Clinical Neurophysiology.

[11]  B. Santhi,et al.  Seizure Detection using SVM Classifier on EEG Signal , 2014 .

[12]  Eric Laciar,et al.  Review: A Survey of Performance and Techniques for Automatic Epilepsy Detection , 2013 .

[13]  Stefano Di Gennaro,et al.  CLASSIFICATION OF EEG SIGNALS FOR DETECTION OF EPILEPTIC SEIZURES BASED ON WAVELETS AND STATISTICAL PATTERN RECOGNITION , 2014 .

[14]  I. Jolliffe.  Principal Component Analysis , 2005 .

[15]  Ajith Abraham,et al.  Artificial Neural Networks , 2005 .

[16]  Ali H. Shoeb,et al.  Application of machine learning to epileptic seizure onset detection and treatment , 2009 .

[17]  Hojjat Adeli,et al.  A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy , 2007, IEEE Transactions on Biomedical Engineering.