Abstract
Epilepsy is a chronic disorder caused in the brain where seizures occur multiple times unreliably causing unconsciousness or tremendous convulsions over the entire body. The identification of epileptic seizure activities in electroencephalography (EEG) signals by manual inspection is prone to errors and time-consuming. The proposed study suggests using Discrete Wavelet Transform to decompose the EEG signals into frequency sub-bands. A certain subset of the frequency sub-bands was chosen for feature selection. Following the DWT decomposition, the proposed method calculates the standard deviation for each sub-band present in the subset. Finally, it feeds the standard deviation values of the sub-bands to the classifiers. This work investigated the three-class classification problem focused on classifying an EEG signal into one of the three classes, which are (1) healthy patient with eyes closed, (2) patients in inter-ictal stage whose EEG recordings have been recorded from the hippocampal formation of the opposite hemisphere of the brain, and (3) patients experiencing seizure activities. The accuracy achieved in proposed work is 98.45% which beats the state-of-the-art accuracy in this three-class problem. Additionally, the proposed method achieves the highest accuracy of 100% in classifying normal EEG signals (eyes open and eyes closed) and seizure EEG signal in two separate experiments which is comparable with the existing state of the art EEG signal classification techniques. The proposed work uses six different classifiers in each of the three experiments conducted where every classifier has been used with 8 different Daubechies wavelets db1 to db8. The results obtained from these experiments provide valuable insights establishing that SVM performs the best in most of the experiments with the db4 wavelet among the 8 wavelets achieving the highest accuracy.
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References
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, 061907 (2001). https://doi.org/10.1103/PhysRevE.64.061907
Bajaj, V., Pachori, R.B.: Classification of seizure and nonseizure EEG signals using empirical mode decomposition. IEEE Trans. Inf. Technol. Biomed. 16(6), 1135–1142 (2012). https://doi.org/10.1109/TITB.2011.2181403
Bhattacharyya, A., Pachori, R.B., Upadhyay, A., Acharya, U.R.: Tunable-q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl. Sci. 7(4) (2017). https://doi.org/10.3390/app7040385. http://www.mdpi.com/2076-3417/7/4/385
Bonati, L.H., Naegelin, Y., Wieser, H.G., Fuhr, P., Ruegg, S.: Beta activity in status epilepticus. Epilepsia 47(1), 207–210 (2006)
Chen, W., Zhuang, J., Yu, W., Wang, Z.: Measuring complexity using FuzzyEn, ApEn, and SampEn. Med. Eng. Phys. 31(1), 61–68 (2009)
Das, A.B., Bhuiyan, M.I.H., Alam, S.M.S.: Classification of EEG signals using normal inverse Gaussian parameters in the dual-tree complex wavelet transform domain for seizure detection. Signal Image Video Process. 10(2), 259–266 (2016). https://doi.org/10.1007/s11760-014-0736-2
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 (2015)
Gandhi, T., Panigrahi, B.K., Anand, S.: A comparative study of wavelet families for EEG signal classification. Neurocomputing 74(17), 3051–3057 (2011). https://doi.org/10.1016/j.neucom.2011.04.029
Geva, A.B., Kerem, D.H.: Forecasting generalized epileptic seizures from the EEG signal by wavelet analysis and dynamic unsupervised fuzzy clustering. IEEE Trans. Biomed. Eng. 45(10), 1205–1216 (1998). https://doi.org/10.1109/10.720198
Guo, L., Rivero, D., Pazos, A.: Epileptic seizure detection using multiwavelet transform based approximate entropy and artificial neural networks. J. Neurosci. Methods 193(1), 156–163 (2010)
Haddad, T., Ben-Hamida, N., Talbi, L., Lakhssassi, A., Aouini, S.: Temporal epilepsy seizures monitoring and prediction using cross-correlation and chaos theory. Healthcare Technol. Lett. 1(1), 45–50 (2014)
Kaya, Y., Uyar, M., Tekin, R., Yıldırım, S.: 1d-local binary pattern based feature extraction for classification of epileptic EEG signals. Appl. Math. Comput. 243, 209–219 (2014)
Nicolaou, N., Georgiou, J.: Detection of epileptic electroencephalogram based on permutation entropy and support vector machines. Expert Syst. Appl. 39(1), 202–209 (2012)
Orhan, U., Hekim, M., Ozer, M.: EEG signals classification using the k-means clustering and a multilayer perceptron neural network model. Expert Syst. Appl. 38(10), 13475–13481 (2011). https://doi.org/10.1016/j.eswa.2011.04.149
Peker, M., Sen, B., Delen, D.: A novel method for automated diagnosis of epilepsy using complex-valued classifiers. IEEE J. Biomed. Health Inform. 20(1), 108–118 (2016). https://doi.org/10.1109/JBHI.2014.2387795
Ren, L., Kucewicz, M.T., Cimbalnik, J., Matsumoto, J.Y., Brinkmann, B.H., Hu, W., Marsh, W.R., Meyer, F.B., Stead, S.M., Worrell, G.A.: Gamma oscillations precede interictal epileptiform spikes in the seizure onset zone. Neurology 84(6), 602–608 (2015)
Samiee, K., Kovcs, P., Gabbouj, M.: Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform. IEEE Trans. Biomed. Eng. 62(2), 541–552 (2015). https://doi.org/10.1109/TBME.2014.2360101
Sharma, M., Pachori, R.B., Acharya, U.R.: A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recognit. Lett. 94, 172–179 (2017). https://doi.org/10.1016/j.patrec.2017.03.023
Sharmila, A., Geethanjali, P.: DWT based detection of epileptic seizure from EEG signals using naive Bayes and k-NN classifiers. IEEE Access 4, 7716–7727 (2016). https://doi.org/10.1109/ACCESS.2016.2585661
Subasi, A., Kevric, J., Abdullah Canbaz, M.: Epileptic seizure detection using hybrid machine learning methods. Neural Comput. Appl. 31(1), 317–325 (2019). https://doi.org/10.1007/s00521-017-3003-y
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). https://doi.org/10.1016/j.eswa.2016.02.040
Tiwari, A.K., Pachori, R.B., Kanhangad, V., Panigrahi, B.K.: Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals. IEEE J. Biomed. Health Inform. 21(4), 888–896 (2017). https://doi.org/10.1109/JBHI.2016.2589971
Tzallas, A.T., Tsipouras, M.G., Fotiadis, D.I.: Automatic seizure detection based on time-frequency analysis and artificial neural networks. Comput. Intell. Neurosci. 2007 (2007)
Yuan, Q., Zhou, W., Li, S., Cai, D.: Epileptic EEG classification based on extreme learning machine and nonlinear features. Epilepsy Res. 96(1–2), 29–38 (2011)
Acknowledgements
We would like to thank and offer sincere gratitude to Dr. Vibha Sharma and Dr. Puneet Talwar (IHBAS, Delhi) who have guided us with their patience and knowledge throughout the research work.
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Gupta, D., Sethia, D., Gupta, A., Sharma, T. (2020). A Multiclass Classification of Epileptic Activity in Patients Using Wavelet Decomposition. In: Agarwal, S., Verma, S., Agrawal, D. (eds) Machine Intelligence and Signal Processing. MISP 2019. Advances in Intelligent Systems and Computing, vol 1085. Springer, Singapore. https://doi.org/10.1007/978-981-15-1366-4_33
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DOI: https://doi.org/10.1007/978-981-15-1366-4_33
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