Local Transformed Features for Epileptic Seizure Detection in EEG Signal
Epilepsy is a well known neurological disorder characterized by the presence of recurrent seizures. Electroencephalograms (EEGs) record electrical activity in the brain and are used to detect epilepsy. Traditional EEG analysis methods for epileptic seizure detection are time-consuming, which has led to the recent proposal of several automated seizure detection frameworks. Feature extraction and classification are two important steps in this procedure. Feature extraction focuses on finding the informative features that could be used in the classification step for correct decision making; therefore, proposing some effective feature extraction techniques for seizure detection is of great significance. This paper introduces two novel feature extraction techniques: local centroid pattern (LCP) and one-dimensional local ternary pattern (1D-LTP) for seizure detection in EEG signal. Both the techniques are computationally simple and easy to implement. In both the techniques, the histograms are formed in the first step using the transformation code and then these histogram-based feature vectors are fed into a classifier in the second step. The performance of the proposed techniques was evaluated through 10-fold cross-validation tested on the benchmark dataset. Different machine learning classifiers were used for the classification. The experimental results show that LCP and 1D-LTP achieved the highest accuracy of 100% for the classification between normal and seizure EEG signals with the artificial neural network classifier. Nine different experimental cases have been tested. The results achieved for different experimental cases were higher than the results of some existing techniques in the literature. The experimental results indicate that LCP and 1D-LTP could be effective feature extraction techniques for seizure detection.
KeywordsElectroencephalogram (EEG) signals Local centroid pattern (LCP) One-dimensional local ternary pattern (1D-LTP) Feature extraction Classification
The authors would like to thank Dr. R.G. Andrzejak of University of Bonn, Germany, for providing permission to use the EEG dataset available online.
Compliance with Ethical Standards
Conflict of interest
All authors declare that they do not have any real or perceived conflicts of interest pertaining to the present study.
- 1.World Health Organization, Fact Sheet. (2016). Epilepsy. Retrieved June, 2016 from http://www.who.int/mediacentre/factsheets/fs999/en/.
- 31.Pachori, R. B., Sharma, R., & Patidar, S. (2015). Classification of normal and epileptic seizure EEG signals based on empirical mode decomposition. In Quanmin Zhu & Ahmad Taher Azar (Eds.), Complex system modelling and control through intelligent soft computations (pp. 367–388). Cham: Springer.Google Scholar
- 33.Bajaj, V., & Pachori, R. B. (2012). Separation of rhythms of EEG signals based on Hilbert-Huang transformation with application to seizure detection. In G. Lee, D. Howard, J. J. Kang, & D. Ślęzak (Eds.), Convergence and hybrid information technology. ICHIT 2012 (Vol. 7425)., Lecture Notes in Computer Science Berlin: Springer.Google Scholar
- 35.Chatlani, N., & Soraghan, J. J. (2010). Local binary patterns for 1-D signal processing. In 18th European Signal Processing Conference, Aalborg, pp. 95–99.Google Scholar
- 42.Kohavi, R. (1995). A study of cross-validation and bootstrap for accuracy estimation and model selection. In Proceedings of the 14th international joint conference on Artificial intelligence—Volume 2 (IJCAI’95) (pp. 1137–1143). San Francisco, CA: Morgan Kaufmann Publishers Inc.Google Scholar
- 43.Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, 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(6), 061907.CrossRefGoogle Scholar