Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform

[1] Annegers, J.F., Rocca, W.A., Hauser, W.A. (1996). Causes of epilepsy: Contributions of the Rochester epidemiology project. In Mayo Clinic Proceedings, 71 (6), 570-575.10.4065/71.6.570 Search in Google Scholar

[2] Birjandtalab, J., Heydarzadeh, M., Nourani, M. (2017). Automated EEG-based epileptic seizure detection using deep neural networks. In IEEE International Conference on Healthcare Informatics (ICHI). IEEE, vol. 1, 552-555. ISBN 978-1-5090-4881-6. Search in Google Scholar

[3] Chen, D., Wan, S., Bao, F.S. (2016). Epileptic focus localization using discrete wavelet transform based on interictal intracranial EEG. In IEEE Transactions on Neural Systems and Rehabilitation Engineering, 25 (5), 413-425.10.1109/TNSRE.2016.2604393 Search in Google Scholar

[4] Yuan, Y., Xun, G., Jia, K., Zhang, A. (2017). A multi-view deep learning method for epileptic seizure detection using short-time fourier transform. In Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics. New York, US: Association for Computing Machinery, 213-222. ISBN 978-1-4503-4722-8.10.1145/3107411.3107419 Search in Google Scholar

[5] Li, M., Chen, W., Zhang, T. (2016). Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM. In Biocybernetics and Biomedical Engineering, 36 (4), 708-718.10.1016/j.bbe.2016.07.004 Search in Google Scholar

[6] Fernandes, S.L., Bala, J.G. (2017). A novel decision support for composite sketch matching using fusion of probabilistic neural network and dictionary matching. In Current Medical Imaging, 13 (2), 176-184.10.2174/1573405612666160606143938 Search in Google Scholar

[7] Raza, M., Sharif, M., Yasmin, M., Khan, M.A., Saba, T., Fernandes, S.L. (2018). Appearance based pedestrians’ gender recognition by employing stacked auto encoders in deep learning. In Future Generation Computer Systems, 88, 28-39.10.1016/j.future.2018.05.002 Search in Google Scholar

[8] Petrosian, A., Prokhorov, D., Homan, R., Dasheiff, R., Wunsch II, D. (2000). Recurrent neural network based prediction of epileptic seizures in intra-and extracranial EEG. In Neurocomputing, 30 (1-4), 201-218.10.1016/S0925-2312(99)00126-5 Search in Google Scholar

[9] Tsiouris, Κ.Μ., Pezoulas, V.C., Zervakis, M., Konitsiotis, S., Koutsouris, D.D., Fotiadis, D.I. (2018). A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. In Computers in Biology and Medicine, 99, 24-37.10.1016/j.compbiomed.2018.05.01929807250 Search in Google Scholar

[10] Abbasi, M.U., Rashad, A., Basalamah, A., Tariq, M. (2019). Detection of epilepsy seizures in neo-natal EEG using LSTM architecture. In IEEE Access, 7, 179074-179085.10.1109/ACCESS.2019.2959234 Search in Google Scholar

[11] Abdelhameed, A.M., Daoud, H.G., Bayoumi, M. (2018). Deep convolutional bidirectional LSTM recurrent neural network for epileptic seizure detection. In 16th IEEE International New Circuits and Systems Conference (NEWCAS). IEEE, 139-143. ISBN 9781538615133.10.1109/NEWCAS.2018.8585542 Search in Google Scholar

[12] San-Segundo, R., Gil-Martín, M., D’Haro-Enriquez, L.F., Pardo, J.M. (2019). Classification of epileptic EEG recordings using signal transforms and convolutional neural networks. In Computers in Biology and Medicine, 109, 148-158.10.1016/j.compbiomed.2019.04.03131055181 Search in Google Scholar

[13] Khan, H., Marcuse, L., Fields, M., Swann, K., Yener, B. (2017). Focal onset seizure prediction using convolutional networks. In IEEE Transactions on Biomedical Engineering, 65 (9), 2109-2118.10.1109/TBME.2017.2785401 Search in Google Scholar

[14] Zhou, M., Tian, C., Cao, R., Wang, B., Niu, Y., Hu, T., Guo, H., Xiang, J. (2018). Epileptic seizure detection based on EEG signals and CNN. In Frontiers in Neuroinformatics, 12, 95.10.3389/fninf.2018.00095629545130618700 Search in Google Scholar

[15] Das, A.B., Bhuiyan, M.I.H. (2016). Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. In Biomedical Signal Processing and Control, 29, 11-21.10.1016/j.bspc.2016.05.004 Search in Google Scholar

[16] Gautam, S., Sriya, S., Chauhan, T. (2015). Focal and non-focal epilepsy detection using eeg signals via empirical mode decomposition. In International Conference on Signal Processing and Communication (ICSC). IEEE, 452-455. ISBN 978-1-4799-6761-2.10.1109/ICSPCom.2015.7150696 Search in Google Scholar

[17] Pachori, R.B., Bajaj, V. (2011). Analysis of normal and epileptic seizure EEG signals using empirical mode decomposition. In Computer Methods and Programs in Biomedicine, 104 (3), 373-381.10.1016/j.cmpb.2011.03.00921529981 Search in Google Scholar

[18] Bizopoulos, P.A., Tsalikakis, D.G., Tzallas, A.T., Koutsouris, D.D., Fotiadis, D.I. (2013). EEG epileptic seizure detection using k-means clustering and marginal spectrum based on ensemble empirical mode decomposition. In 13th IEEE International Conference on Bioinformatics and Bioengineering. IEEE, 1-4. ISBN 978-1-4799-3163-7.10.1109/BIBE.2013.6701528 Search in Google Scholar

[19] Hassan, A.R., Subasi, A., Zhang, Y. (2020). Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise. In Knowledge-Based Systems, 191, 105333.10.1016/j.knosys.2019.105333 Search in Google Scholar

[20] Li, M., Chen, W., Zhang, T. (2017). Classification of epilepsy EEG signals using DWT-based envelope analysis and neural network ensemble. In Biomedical Signal Processing and Control, 31, 357-365.10.1016/j.bspc.2016.09.008 Search in Google Scholar

[21] Kumar, Y., Dewal, M., Anand, R.S. (2014). Epileptic seizures detection in EEG using DWT-based ApEn and artificial neural network. In Signal, Image and Video Processing, 8 (7), 1323-1334.10.1007/s11760-012-0362-9 Search in Google Scholar

[22] Sharmila, A., Aman Raj, S., Shashank, P., Mahalakshmi, P. (2018). Epileptic seizure detection using DWT-based approximate entropy, Shannon entropy and support vector machine: A case study. In Journal of Medical Engineering & Technology, 42 (1), 1-8.10.1080/03091902.2017.139438929251059 Search in Google Scholar

[23] Anand, S.V., Selvakumari, R.S. (2019). Noninvasive method of epileptic detection using DWT and generalized regression neural network. In Soft Computing, 23 (8), 2645-2653.10.1007/s00500-018-3630-y Search in Google Scholar

[24] Gupta, V., Bhattacharyya, A., Pachori, R.B. (2020). Automated identification of epileptic seizures from EEG signals using FBSE-EWT method. In Biomedical Signal Processing. Springer, 157-179. ISBN 978-981-13-9096-8.10.1007/978-981-13-9097-5_8 Search in Google Scholar

[25] Bhattacharyya, A., Sharma, M., Pachori, R.B., Sircar, P., Acharya, U.R. (2018). A novel approach for automated detection of focal EEG signals using empirical wavelet transform. In Neural Computing and Applications, 29 (8), 47-57.10.1007/s00521-016-2646-4 Search in Google Scholar

[26] Thilagaraj, M., Rajasekaran, M.P. (2018). Epileptic seizure mining via novel empirical wavelet feature with J48 and KNN classifier. In Intelligent Engineering Informatics. Springer, 221-228. ISBN 978-981-10-7565-0.10.1007/978-981-10-7566-7_23 Search in Google Scholar

[27] Dietterich, T.G. (2002). Ensemble learning. In The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press, 110-125. ISBN 9780262011976. Search in Google Scholar

[28] Zhou, Z.-H. (2009). Ensemble learning. In Encyclopedia of Biometrics. Springer, 270-273. ISBN 978-0-387-73002-8.10.1007/978-0-387-73003-5_293 Search in Google Scholar

[29] Polikar, R. (2012). Ensemble learning. In Ensemble Machine Learning. Springer, 1-34. ISBN 978-1-4419-9326-7.10.1007/978-1-4419-9326-7_1 Search in Google Scholar

[30] Sagi, O., Rokach, L. (2018). Ensemble learning: A survey. In WIREs : Data Mining and Knowledge Discovery, 8 (4), e1249.10.1002/widm.1249 Search in Google Scholar

[31] Ullah, I., Hussain, M., Aboalsamh, H. (2018). An automated system for epilepsy detection using EEG brain signals based on deep learning approach. In Expert Systems with Applications, 107, 61-71.10.1016/j.eswa.2018.04.021 Search in Google Scholar

[32] Abdulhay, E., Elamaran, V., Chandrasekar, M., Balaji, V.S., Narasimhan, K. (2020). Automated diagnosis of epilepsy from EEG signals using ensemble learning approach. In Pattern Recognition Letters, 139, 174-181.10.1016/j.patrec.2017.05.021 Search in Google Scholar

[33] Rahman, M.M., Bhuiyan, M.I.H., Das, A.B. (2019). Classification of focal and non-focal EEG signals in VMD-DWT domain using ensemble stacking. In Biomedical Signal Processing and Control, 50, 72-82.10.1016/j.bspc.2019.01.012 Search in Google Scholar

[34] Boonyakitanont, P., Lek-Uthai, A., Chomtho, K., Songsiri, J. (2020). A review of feature extraction and performance evaluation in epileptic seizure detection using EEG. In Biomedical Signal Processing and Control, 57, 101702.10.1016/j.bspc.2019.101702 Search in Google Scholar

[35] Gilles, J. (2013). Empirical wavelet transform. In IEEE Transactions on Signal Processing, 61 (16), 3999-4010.10.1109/TSP.2013.2265222 Search in Google Scholar

[36] Huang, N.E., Shen, Z., Long, S.R., Wu, M.C., Shih, H.H., Zheng, Q., Yen, N.-C., Tung, C.C., Liu. H.H. (1998). The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. In Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 454 (1971), 903-995.10.1098/rspa.1998.0193 Search in Google Scholar

[37] Carvalho, V.R., Moraes, M.F., Braga, A.P., Mendes, E.M. (2020). Evaluating five different adaptive decomposition methods for EEG signal seizure detection and classification. In Biomedical Signal Processing and Control, 62, 102073.10.1016/j.bspc.2020.102073 Search in Google Scholar

[38] Welch, P. (1967). The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms. In IEEE Transactions on Audio and Electroacoustics, 15 (2), 70-73.10.1109/TAU.1967.1161901 Search in Google Scholar

[39] Andrzejak, R.G., Lehnertz, K., Mormann, F., Rieke, C., David, P., Elger, C. (2001). Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. In Physical Review E, 64 (6), 061907.10.1103/PhysRevE.64.06190711736210 Search in Google Scholar

[40] Gandhi, T.K., Chakraborty, P., Roy, G.G., Panigrahi, B.K. (2012). Discrete harmony search based expert model for epileptic seizure detection in electroencephalography. In Expert Systems with Applications, 39 (4), 4055-4062.10.1016/j.eswa.2011.09.093 Search in Google Scholar