DETECTION OF PRE-EPILEPTIC SEIZURE BY USING WAVELET DECOMPOSITION AND ARTIFICIAL NEURAL NETWORKS

  • Talha Burak Alakus Department of Software Engineering, Kirklareli University
  • Ibrahim Turkoglu Department of Software Engineering, Firat University
Keywords: Epilepsy, Signal processing, Artificial neural network, Entropy, Classification.

Abstract

Epilepsy is one of the most frequent disease all over the world and can give valuable information about the functional situation of the brain. This information is clear when EEG signals are collected and analyzed in an appropriate, accurate and reliable way and they make detection of epileptic seizures is possible. It is difficult to interfere with epileptic seizures because they occur suddenly and randomly thus, detection of epilepsy in the early stage is vital for both patients and medical officials. In this study, firstly both 100 interictal (EEG signals which occur between two epileptic seizures) and 100 healthy EEG signals are normalized in order to detect the pre-epileptic seizure. After the normalization process, each EEG signals are decomposed by performing Wavelet Decomposition (WD) and classified with ANN (Artificial Neural Networks). 4-fold cross-validation technique is used to evaluate the classification performance. LogEn entropy is used to extract features from both interictal and healthy EEG signals and classification accuracy is achieved 97,5%.

Downloads

Download data is not yet available.

References

WHO (2017). Epilepsy [Online].Available: http://www.who.int/mediacentre/factsheets/fs999/en/

Guo, L., Rivero, D., Dorado, J., Rabunal, J. R., Pazoz, A. (2010). ’Automatic Epileptic Seizure Detection in EEGs Based on Line Length Feature and Artificial Neural Networks. Journal of Neuroscience Methods, 191, 101-109.

Mormann, F., Andrzejak, R. G., Elger, C. E., Lehnertz, K. (2007). Seizure Prediction: The Long and Winding Road. In Brain, 130(2), 314-333.

Salem, O., Naseem, A., Mehaoua, A. (2014). Epileptic Seizure Detection from EEG Signal using Discrete Wavelet Transform and Ant Colony Classifier. Selected Areas in Communications Symposium, 3529 – 3534.

Alsharabi, K., Ibrahim, S., Djemal, R., Alsuwailem, A. (2016). A DWT-Entropy Based Architecture for Epilepsy Diagnosis using EEG Signals. 2nd International Conference on Advanced Technologies for Signal Processing and Image Processing – ATSIP, 288 – 291.

Dilber, D., Kaur, J. (2016). EEG Based Detection for Epilepsy by a Mixed Design Approach. IEEE International Conference on Recent Trends in Electronics Information Communication Technology, 1425 – 1428.

Damayanti, A., Pratiwi, A. B., Miswanto. (2016). Epilepsy Detection on EEG Data using Backpropagation Firefly Algorithm and Simulated Annealing. 2nd International Conference on Science and Technology Computer – ICST.

Ozkan, C., Dogan, S., Kantar, T., Akşahin, M. F., Erdamar, A. (2016). Detection of Epilepsy Disease from EEG Signals with Artificial Neural Network. 24th Signal Processing and Communication EEG Application Conference – SIU.

Hamad, A., Houssein, E. H., Hassanien, A. E., Fahmy, A. A. (2016). Feature Extraction of Epilepsy EEG using Discrete Wavelet Transform. 12th International Computer Engineering Conference – ICENCO, 190 – 195.

Abdulhay, E., Elamaran, V., Chandrasekar, M., Balaji, V. S., Narasimhan, K. (2017). Automated Diagnosis of Epilepsy from EEG Signals using Ensemble Learning Approach. Pattern Recognition Letters.

Tiwari, A. K., Pachori, R. B., Kanhangad, V., Panighrahi, B. K. (2017). Automated Diagnosis of Epilepsy using Key-Point Based Local Binary Pattern of EEG Signals. IEEE Journal of Biomedical and Health Informatics, 21(4), 888 – 896.

Kocadadli, O., Langari, R. (2017). Classification of EEG Signals for Epileptic Seizures using Hybrid Artificial Neural Networks Based Wavelet Transforms and Fuzzy Relations. Expert Systems with Applications, 88, 419 – 434.

Satapathy, S. K., Dehuri, S., Jagadev, A. K. (2017). EEG Signal Classification using PSO Trained RBF Neural Network for Epilepsy Identification. Informatics in Medicine Unlocked, 6, 1 – 11.

Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., David, F. P., Elger, C. E. (2001). Indications of Nonlinear Deterministic and Finite-Dimensional Structures in Time Series of Brain Electrical Activity: Dependence on Recording Region and Brain State. Physical Review E, 64, 64 – 71.

Shannon, C. E. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27(3), 379 – 423.

Aydin, S., Saraglo, H. M., Karak, S. (2009). Log Energy Entropy-Based EEG Classification with Multiplayer Neural Network in Seizure. Annals of Biomedical Engineering, 37(12), 2626 – 2630.

Sartoretto, F., Ermani, M. (1999). Automatic Detection of Epileptiform activity by Single-Level Wavelet Analysis. Clinical Neurophysiology, 11(2), 239 – 249.

Published
2018-06-29
How to Cite
Burak Alakus, T., & Turkoglu, I. (2018). DETECTION OF PRE-EPILEPTIC SEIZURE BY USING WAVELET DECOMPOSITION AND ARTIFICIAL NEURAL NETWORKS. International Journal of Scientific Research in Information Systems and Engineering (IJSRISE), 4(1), 31-36. Retrieved from http://ijsrise.com/index.php/IJSRISE/article/view/6
Abstract viewed = 0 times
PDF downloaded = 0 times

Most read articles by the same author(s)

Obs.: This plugin requires at least one statistics/report plugin to be enabled. If your statistics plugins provide more than one metric then please also select a main metric on the admin's site settings page and/or on the journal manager's settings pages.