Open Access Open Access  Restricted Access Subscription or Fee Access

Fast Detection of Brain Disorders using EEG Signal

M. S. Jamuar, A. Shah, M. Kucic

Abstract


EEG is brain signal processing technique that allows gaining the understanding of the complex inner mechanisms of the brain and abnormal brain waves have shown to be associated with particular brain disorders. The analysis of brain waves plays an important role in diagnosis of different brain disorders. The electroencephalogram (EEG) is the recording of electrical activity of the brain; it is a very effective tool for understanding the complex dynamical behavior of the brain. EEG signal towards the detection of abnormalities follows mainly four stages: pre-processing, feature extraction, feature selection, classification and ischemic episode recognition. The performance of detection shows better accuracy than the existing.


Keywords


Electroencephalogram, Pre-Processing, Feature Selection, Feature Extraction, Classification

Full Text:

PDF

References


Clodoaldo A M, Andre L V Coelho, Marcio Eisencraft, (2010) 'Tackling EEG signal classification with least squares support vector machines: A sensitivity analysis study’, Elsevier Transactions on computers in biology medicine, Vol.40, pp.705-714.

Bosl, William, Adrienne Tierney, Helen Tager-Flusberg, and Charles Nelson. "EEG complexity as a biomarker for autism spectrum disorder risk." BMC medicine 9, no. 1 (2011): 18.

E Parvinnia, M Sabeti, M Zolghadri Jahromi, R Boostani, (2013) 'Classification of EEG Signals using adaptive weighted distance nearest neighbor algorithm', Transactions Computer and Information Sciences.

Marcus Musselman, Dragan Djurdjanovic, (2012) 'Time–frequency distributions in the classification of epilepsy from EEG signals' Elsevier Transactions on Expert Systems with Applications, Vol.37, pp.11413-11422.

M Sabeti, R Boostani, S D Katebi, G W Price, (2007)’ Selection of relevant features for EEG signal classification of schizophrenic patients’, Elsevier Transactions on biomedical signal processing, Vol.28, pp.122-134.

Nandish.M, Stafford Michahial, Hemanth Kumar P, Faizan Ahmed, (2012) 'Feature Extraction and Classification of EEG Signal Using Neural Network Based Techniques', Transactions on International Journal of Engineering and Innovative Technology (IJEIT), Vol.2.

Nurujjaman Md, Ramesh Narayanan and Sekar Iyengar A N, (2009) ‘Comparative study of nonlinear properties of EEG signals of normal persons and epileptic patients’, Transactions on Nonlinear Biomedical physics.

Dauwels, Justin, François Vialatte, and Andrzej Cichocki. "Diagnosis of Alzheimer's disease from EEG signals: where are we standing?." Current Alzheimer Research 7, no. 6 (2010): 487-505.

Rami J Oweis and Enas W Abdulhay, (2011)‘Seizure classification in EEG signals utilizing Hilbert-Huang transform’, Transaction on Bio-Medical Engineering OnLine.

Ram Bilas Pachori,Varun Bajaj, ‘Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition’(2011), Elsevier Transactions on Computer Methods and program in biomedicine, Vol.104, pp.373-381

Siuly siuly and Yan Li, Member, IEEE, (2012) ‘Improving the Separability of Motor Imagery EEG Signals Using a Cross Correlation-Based Least Square Support Vector Machine for brain-Computer Interface’, IEEE Transactions on neural systems and rehabilitation engineering, Vol.20, No.4, pp.526-538.

Stevenson N J, I.Korotchikova, A Temko, G Lightbody, W P Marnane and G B Boylan, (2013) ’An Automated System for Grading EEG Abnormality in Term Neonates with Hypoxic-Ischamic Encephalopathy’ ,Elsevier Transactions on Brain and Development, Vol.41, pp.775-785.

Salih Gunes, Kemal Polat, S_ebnem, (2010) ‘Efficient sleep stage recognition system based on EEG signal using k-means clustering based feature weighting’, Elsevier Transactions on Expert Systems with Applications, Vol.37, pp.7922-7928.

Umut Orhan, Mahmut Hekim Mahmut Ozer, (2011) ‘EEG signals classification using the K-means clustering and a multilayer perceptron neural network model’, Transactions on Expert Systems with Applications, Vol.37, pp.13475–13481.

Guerrero-Mosquera, Carlos, Armando Malanda Trigueros, Jorge Iriarte Franco, and Ángel Navia-Vázquez. "New feature extraction approach for epileptic EEG signal detection using time-frequency distributions." Medical & biological engineering & computing 48, no. 4 (2010): 321-330.


Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.