Have a personal or library account? Click to login
Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform Cover

Epileptic Seizure Detection using Deep Ensemble Network with Empirical Wavelet Transform

Open Access
|Aug 2021

References

  1. [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
  2. [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.
  3. [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
  4. [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
  5. [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
  6. [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
  7. [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
  8. [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
  9. [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
  10. [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
  11. [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
  12. [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
  13. [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
  14. [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
  15. [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
  16. [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
  17. [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
  18. [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
  19. [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
  20. [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
  21. [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
  22. [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
  23. [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
  24. [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
  25. [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
  26. [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
  27. [27] Dietterich, T.G. (2002). Ensemble learning. In The Handbook of Brain Theory and Neural Networks, Second Edition. MIT Press, 110-125. ISBN 9780262011976.
  28. [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
  29. [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
  30. [30] Sagi, O., Rokach, L. (2018). Ensemble learning: A survey. In WIREs : Data Mining and Knowledge Discovery, 8 (4), e1249.10.1002/widm.1249
  31. [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
  32. [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
  33. [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
  34. [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
  35. [35] Gilles, J. (2013). Empirical wavelet transform. In IEEE Transactions on Signal Processing, 61 (16), 3999-4010.10.1109/TSP.2013.2265222
  36. [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
  37. [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
  38. [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
  39. [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
  40. [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
Language: English
Page range: 110 - 116
Submitted on: Apr 24, 2021
|
Accepted on: Aug 5, 2021
|
Published on: Aug 13, 2021
In partnership with: Paradigm Publishing Services
Publication frequency: Volume open

© 2021 Sreelekha Panda, Abhishek Das, Satyasis Mishra, Mihir Narayan Mohanty, published by Slovak Academy of Sciences, Institute of Measurement Science
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.