Analysis of EEG Signal for the Detection of Brain Abnormalities
M Kalaivani, V Kalaivani and Anusuya V Devi. Article: Analysis of EEG Signal for the Detection of Brain Abnormalities. IJCA Proceedings on International Conference on Simulations in Computing Nexus ICSCN(2):1-6, May 2014. Full text available. BibTeX
@article{key:article,
author = {M. Kalaivani and V. Kalaivani and V. Anusuya Devi},
title = {Article: Analysis of EEG Signal for the Detection of Brain Abnormalities},
journal = {IJCA Proceedings on International Conference on Simulations in Computing Nexus},
year = {2014},
volume = {ICSCN},
number = {2},
pages = {1-6},
month = {May},
note = {Full text available}
}
Abstract
In the field of medical science, one of the major ongoing researches is the diagnosis of the abnormalities in brain. The Electroencephalogram (EEG) is a tool for measuring the brain activity which reflects the condition of the brain. EEG is very effective tool for understanding the complex behaviour of the brain. The aim of this study is to classify the EEG signal as normal or abnormal. It is proposed to develop an automated system for the classification of brain abnormalities. The proposed system includes pre-processing, feature extraction, feature selection and classification. In pre-processing the noises are removed. The discrete wavelet transform is used to decompose the EEG signal into sub-band signals. The feature extraction methods are used to extract the time domain and frequency domain features of the EEG signal.
References
- AbdulhamitSubasi and Ismail M Gursoy (2010)'EEG Signal Classification Using PCA, ICA, LDA and Support Vector Machines' Elsevier Transactions on Expert Systems with applications Vol. 37pp. 8659-8666.
- Abdulhamit and Subasi (2005) 'Epileptic Seizure Detection Using Dynamic Wavelet Network' ElsevierTransactions on Expert Systems with Applications Vol. 29 pp. 343-355.
- AbdulhamitSubasi and Ergun Ercelebi (2005) 'Classification of EEG Signals Using Neural Network and Logistic Regression' Elsevier Transactions onComputer Methods and Programs in Biomedicine Vol. 78 pp. 87-99.
- BehshadHosseinifarda, Mohammad Hassan Moradia and Reza Rostamib (2013) 'Classifying Depression Patients and Normal Subjects Using Machine Learning Techniques and Nonlinear Features from EEG Signal', Elsevier Transactions on Computer methods and Programs Vol. 109 pp. 339-345.
- ClodoaldoA. M. Limaa and Andre L. V. Coelho (2011) 'Kernel Machines for Epilepsy Diagnosis via EEG Signal Classification', Elsevier Transactions on Artificial Intelligence in Medicine Vol. 53 pp. 83-95.
- Clodoaldo A. M. Lima, Andre L. V. Coelho andMarcioEisencraft(2010) 'Tackling EEG Signal Classification with Least Squares Support Vector Machines: A Sensitivity Analysis Study', Elsevier Transactions on Computers in Biology and Medicine Vol. 40 pp. 705-714.
- Clodoaldo A. M. Lima, André L. V. Coelho and Sandro Chagas(2009) 'Automatic EEG Signal Classification for Epilepsy Diagnosis With Relevance Vector Machines', Elsevier Transactions on Expert Systems with Applications Vol. 36 pp. 10054-10059.
- Deng Wang, Duoqian Miao and Chen Xie (2011) 'Best Basis-based Wavelet Packet Entropy Feature Extraction and Hierarchical EEG Classification for Epileptic Detection', Elsevier Transactions on Expert systems with Applications Vol. 38 pp. 14314-14320.
- Kai-Cheng Hsu andSung-Nien Yu (2010) 'Detection of Seizures in EEG Using Subband Nonlinear Parameters and Genetic Algorithm', ELSEVIER Transactions on Biology and Medicine Vol. 40 pp. 823-230.
- KhadijehSadatnezhad, Reza Boostani and Ahmad Ghanizadeh (2011)'Classification of BMD and ADHD Patients Using Their EEG Signals', Elsevier Transactions on Expert Systems with ApplicationsVol. 38 pp. 1956-1963.