Abstract
Selection of suitable order of Daubechies (DB) wavelet for the decomposition of Electroencephalogram (EEG) signals to detect epileptic seizures is quite challenging, as experimentation is time-consuming. In existing methods, the selection of basis wavelet function for decomposition of EEG signals is carried out by considering the literature or by trial and error method. There is a very little significant literature which discusses the comparative analysis for the identification of suitable basis wavelet function (mother wavelet). However, the existing methods often fail to provide proper justification for selecting the mother wavelets. Hence, this research work addresses the fore-mentioned setback by identifying the suitable basis wavelet function based on wavelet selection methods for epileptic seizure detection. Further, the entropy-based features are extracted and classified using SVM, DT, ANN, and KNN with five complex cases (University of Bonn, Germany EEG dataset): A-B-C-D-E, AB-CD-E, C-D-E, AB-C-D, and ABCD-E. From the entropy analysis, it is evident that while extracting entropy-based features, tenth order of Daubechies wavelet (DB10) is found to be the most suitable basis wavelet function for the accurate detection of Epileptic Seizures. The performance metrics confirm the suitability of the identified basis wavelet function in terms of sensitivity, specificity and classification accuracy.
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References
Li, M., Chen, W., Zhang, T.: Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM. Biocybernetics Biomed. Eng. 36(4), 708–718 (2016)
WHO Homepage: https://www.who.int/news-room/fact-sheets/detail/epilepsy. Last accessed 7 Feb 2019
Kocadagli, O., Langari, R.: Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations. Expert Syst. Appl. 88, 419–434 (2017)
Sharma, M., Pachori, R.B., Acharya, U.Rajendra: A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn. Lett. 94, 172–179 (2017)
Mursalin, M., Zhang, Y., Chen, Y., Chawla, N.V.: Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier. Neurocomputing 241, 204–214 (2017)
Li, M., Chen, W., Zhang, T.: Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. Biomed. Sig. Process. Control 31, 357–365 (2017)
Gajic, D., Djurovic, Z., Gligorijevic, J., Di Gennaro, S., Savic-Gajic, I.: Detection of epileptiform activity in EEG signals based on time-frequency and non-linear analysis. Front. Comput. Neurosci. 9(38), 1–16 (2015)
Al Ghayab, H.R., Li, Y., Siuly, S., Abdulla, S.: A feature extraction technique based on tunable Q-factor wavelet transform for brain signal classification. J. Neurosci. Methods 312, 43–52 (2019)
Sharma, M., Bhurane, A.A., Acharya, U.Rajendra: MMSFL-OWFB: a novel class of orthogonal wavelet filters for epileptic seizure detection. Knowl.-Based Syst. 160, 265–277 (2018)
Hassan, A.R., Siuly, S., Zhang, Y.: Epileptic seizure detection in EEG signals using tunable-Q factor wavelet transform and bootstrap aggregating. Comput. Methods Programs Biomed. 137, 247–259 (2016)
Swami, P., Gandhi, T.K., Panigrahi, B.K., Tripathi, M., Anand, S.: A novel robust diagnostic model to detect seizures in electroencephalography. Expert Syst. Appl. 56, 116–130 (2016)
Nunes, T.M., Coelho, A.L.V., Lima, C.A.M., Papa, J.P., De Albuquerque, V.H.C.: EEG signal classification for epilepsy diagnosis via optimum path forest—a systematic assessment. Neurocomputing 136, 103–123 (2014)
Zaeri, R., Ghanbarzadeh, A., Attaran, B., Moradi, S.: Artificial neural network based fault diagnostics of rolling element bearings using continuous wavelet transform. In: The 2nd International Conference on Control, Instrumentation and Automation, Shiraz, Iran, pp. 753–758 (2011)
Megahed, A.I., Moussa, A.M., Elrefaie, H.B., Marghany, Y.M.: Selection of a suitable mother wavelet for analyzing power system fault transients. In: IEEE Power and Energy Society General Meeting-Conversion and Delivery of Electrical Energy in the 21st Century. IEEE/Institute of Electrical and Electronics Engineers Incorporated, pp. 1–7 (2008)
Rodrigues, A.P., Mello, G.D., Pai, P.Srinivasa: Selection of mother wavelet for wavelet analysis of vibration signals in machining. J. Mech. Eng. Autom. 6(5A), 81–85 (2016)
Acharya, U.R., Fujita, H., Sudarshan, V.K., Bhat, S., Koh, J.E.W.: Application of entropies for automated diagnosis of epilepsy using EEG signals: A review. Knowl.-Based Syst. 88, 85–96 (2015)
Noorizadeh, S., Shakerzadeh, E.: Shannon entropy as a new measure of aromaticity, Shannon aromaticity. Phys. Chem. Chem. Phys. 12(18), 4742–4749 (2010)
Jain, S., Shukla, S., Wadhvani, R.: Dynamic selection of normalization techniques using data complexity measures. Expert Syst. Appl. 106, 252–262 (2018)
Riaz, F., Hassan, A., Rehman, S., Niazi, I.K., Dremstrup, K.: EMD-based temporal and spectral features for the classification of EEG signals using supervised learning. IEEE Trans. Neural Syst. Rehabil. Eng. 24(1), 28–35 (2016)
Kalbkhani, H., Shayesteh, M.G.: Stockwell transform for epileptic seizure detection from EEG signals. Biomed. Sig. Process. Control 38, 108–118 (2017)
Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C.E.: Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: dependence on recording region and brain state. Phys. Rev. E 64(6), 1–8 (2001)
Acknowledgements
This work was supported by the IBM Shared University Research Grant and the Department of Science and Technology, India through Fund for Improvement of S&T Infrastructure (FIST) Programme (SR/FST/ETI-349/2013).
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Anila Glory, H., Vigneswaran, C., Shankar Sriram, V.S. (2020). Identification of Suitable Basis Wavelet Function for Epileptic Seizure Detection Using EEG Signals. In: Luhach, A., Kosa, J., Poonia, R., Gao, XZ., Singh, D. (eds) First International Conference on Sustainable Technologies for Computational Intelligence. Advances in Intelligent Systems and Computing, vol 1045. Springer, Singapore. https://doi.org/10.1007/978-981-15-0029-9_48
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