Study of EEG Signal for Epilepsy Detection and Localization Using Bagged Tree and SVM Algorithms

Mostafa A. Abd-ElBaset; Sherif H. ElGohary

Authors

Mostafa A. Abd-ElBaset
Sherif H. ElGohary

Keywords:

Abstract

Epilepsy is considered one of the common medical and social disorders with unique characteristics. EEG signal was used for the classification and detection of epilepsy. This study proposed epilepsy classification without signal decomposition, as well as other algorithms used for decomposing the EEG signal to sub-bands like discrete wavelet transform (DWT) and dual-tree complex wavelet transform (DT-CWT). Descriptive comparisons were done between results for EEG signals with/without decomposition. The proposed algorithm includes the study of the extracted features and using machine learning kernels as Support Vector Machine (SVM) and bagged tree to achieve the optimal values of (accuracy-specificity-sensitivity and execution time). Results show that adding the line length to the group of features, the accuracy increased to 99.4%. By employing decomposing the EEG signal, the accuracy could be raised to99.875 % even after reducing the number of features to only three features. These features are line length, STD, and mean. This study proposed different algorithms with minimum features for epilepsy classification and localization with optimum execution time.

Downloads

How to Cite

Study of EEG Signal for Epilepsy Detection and Localization Using Bagged Tree and SVM Algorithms. (2019). Global Journal of Medical Research, 19(K7), 1-6. https://medicalresearchjournal.org/index.php/GJMR/article/view/1873

Download Citation

References

B Suguna Nanthini, B Santhi (2012) Qualitative diagnostic criteria into objective quantitative signal feature classification. 42, 94-99.

E Kande L, J Schwartz, T Jessell (2000) Principles of Neural Science.

U Misra, J Kalita (2009) Clinical Electroencephalography.

A Subashi (2005) Eileptic seizure detection using dynamic wavelet network. 29(2), 343-355.

A Subashi (2007) EEG signal classification using wavelet feature extraction and a mixture of expert model. 28(4), 701-711.

H Mohseni, A Maghsoudi, M Kadbi, J Hashemi, A Ashourvan (2006) Automatic detection of epileptic seizure using time-frequency distributions. 2006, 29-29.

Ling Guo, Daniel Rivero, Julián Dorado, Juan Rabuñal, Alejandro Pazos (2010) Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks. 191(1), 101-109.

B Suguna Nanthini, B Santhi (2014) Components of Soft Computing for Epileptic Seizure Prediction and Detection. 65-69.

Manisha Chandani, Arun Kumar (2018) EEG signal processing for epileptic seizure Prediction by using MLPNN and SVM Classifiers. 2(2), 36-41.

Ralph Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, Christian Elger (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. 64(6), 61907-61908.

Piotr Zwolinski, Marcin Roszkoski, Jaroslaw Zygierewicz, Stefan Haufe, Guido Nolte, Piotr Durka Open Database of Epileptic with MRI and Post Operational Assessment of Foci-a Real World Verification for the EEG Inverse Solutions.

P Zwoliński, M Roszkowski, J Zygierewicz, S Haufe, G Nolte, P Durka (2010) Open database of epileptic EEG with MRI and post operational assessment of foci-a real world verification for the EEG inverse solutions. 8(4), 285-229.

C Chui (1992) An Introduction to Wavelets.

A Dingle, R Jones, G Carroll, W Fright (1993) A multistage system to detect epileptic form activity in the EEG. 40(12), 1260-1268.

S Deivasigamani, C Senthilpari, Wong Yong (2016) Classification of focal and nonfocal EEG signals using ANFIS classifier for epilepsy detection. 26(4), 277-283.

R Esteller, Echauz, T Tcheng, B Litt (2001) Line length: An efficient feature for seizure onset detection. 4.