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Recent Patents on Engineering

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ISSN (Print): 1872-2121
ISSN (Online): 2212-4047

Research Article

Seizure Prediction on EEG Signals using Feature Augmentation based Multi Model Ensemble

Author(s): A. Anandaraj* and P.J.A. Alphonse

Volume 19, Issue 1, 2025

Published on: 01 November, 2023

Article ID: e011123223032 Pages: 9

DOI: 10.2174/0118722121256663231019061211

Price: $65

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Abstract

Background: Epilepsy is a neurological disorder that leads to seizures. This occurs due to excessive electrical discharge by the brain cells. An effective seizure prediction model can aid in improving the lifestyle of epilepsy patients. After analyzing various patents related to seizure prediction, it is observed that monitoring electroencephalography (EEG) signals of epileptic patients is an important task for the early diagnosis of seizures.

Objective: The main objective of this paper is to assist epileptic patients to enhance their way of living by predicting the seizure in advance.

Methods: This paper builds a feature augmentation-based multi-model ensemble-based architecture for seizure prediction. The proposed technique is divided into 2 broad categories; feature augmentation and ensemble modeling. The feature augmentation process builds temporal features while the multi-model ensemble has been designed to handle the high complexity levels of the EEG data. The first phase of the multi-model ensemble has been designed with heterogeneous classifier models. The second phase is based on the prediction results obtained from the first phase. Experiments were performed using the seizure prediction dataset from the University Hospital of Bonn.

Results: Comparison indicates 98.7% accuracy, with improvement of 5% from the existing model. High prediction levels indicate that the model is highly capable of providing accurate seizure predictions, hence ensuring its applicability in real time.

Conclusion: The result of this paper has been compared with existing methods of predicting seizures and it indicated that the proposed model has better enhancement in the accuracy levels.

Keywords: Seizure prediction, epilepsy, ensemble modelling, multi-model ensemble, feature augmentation, feature engineering, feature selection.

Graphical Abstract
[1]
U.R. Acharya, S.L. Oh, Y. Hagiwara, J.H. Tan, and H. Adeli, "Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals", Comput. Biol. Med., vol. 100, pp. 270-278, 2018.
[http://dx.doi.org/10.1016/j.compbiomed.2017.09.017] [PMID: 28974302]
[2]
Y. Kumar, M.L. Dewal, and R.S. Anand, "Epileptic seizure detection using DWT based fuzzy approximate entropy and support vector machine", Neurocomputing, vol. 133, pp. 271-279, 2014.
[http://dx.doi.org/10.1016/j.neucom.2013.11.009]
[3]
M. Sharanreddy, and P.K. Kulkarni, "Automated EEG signal analysis for identification of epilepsy seizures and brain tumour", J. Med. Eng. Technol., vol. 37, no. 8, pp. 511-519, 2013.
[http://dx.doi.org/10.3109/03091902.2013.837530] [PMID: 24116656]
[4]
T. Zhang, W. Chen, and M. Li, "AR based quadratic feature extraction in the VMD domain for the automated seizure detection of EEG using random forest classifier", Biomed. Signal Process. Control, vol. 31, pp. 550-559, 2017.
[http://dx.doi.org/10.1016/j.bspc.2016.10.001]
[5]
R. Hussein, M. Elgendi, Z.J. Wang, and R.K. Ward, "Robust detection of epileptic seizures based on L1-penalized robust regression of EEG signals", Expert Syst. Appl., vol. 104, pp. 153-167, 2018.
[http://dx.doi.org/10.1016/j.eswa.2018.03.022]
[6]
C.N. Cooray, A. Carvalho, and G.K. Cooray, "Noise induced quiescence of epileptic spike generation in patients with epilepsy", J. Comput. Neurosci., vol. 49, no. 1, pp. 57-67, 2021.
[http://dx.doi.org/10.1007/s10827-020-00772-3] [PMID: 33420615]
[7]
C. Liu, B. Tan, M. Fu, J. Li, J. Wang, F. Hou, and A. Yang, "Automatic sleep staging with a single-channel EEG based on ensemble empirical mode decomposition", Physica A, vol. 567, .125685
2021 [http://dx.doi.org/10.1016/j.physa.2020.125685]
[8]
V.V. Grubov, E. Sitnikova, A.N. Pavlov, A.A. Koronovskii, and A.E. Hramov, "Recognizing of stereotypic patterns in epileptic EEG using empirical modes and wavelets", Physica A, vol. 486, pp. 206-217, 2017.
[http://dx.doi.org/10.1016/j.physa.2017.05.091]
[9]
Z. Gao, Y. Li, Y. Yang, N. Dong, X. Yang, and C. Grebogi, "A coincidence-filtering-based approach for CNNs in EEG-based recognition", IEEE Trans. Industr. Inform., vol. 16, no. 11, pp. 7159-7167, 2020.
[http://dx.doi.org/10.1109/TII.2019.2955447]
[10]
Q. Cai, Z. Gao, J. An, S. Gao, and C. Grebogi, "A graph-temporal fused dual-input convolutional neural network for detecting sleep stages from EEG signals", IEEE Trans. Circuits Syst. II Express Briefs, vol. 68, no. 2, pp. 777-781, .
2021 [http://dx.doi.org/10.1109/TCSII.2020.3014514]
[11]
A. Zarei, and B.M. Asl, "Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based fea-tures of EEG signals", Comput. Biol. Med., vol. 131, .104250
2021 [http://dx.doi.org/10.1016/j.compbiomed.2021.104250] [PMID: 33578071]
[12]
M. Savadkoohi, T. Oladunni, and L. Thompson, "A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal", Biocybern. Biomed. Eng., vol. 40, no. 3, pp. 1328-1341, 2020.
[http://dx.doi.org/10.1016/j.bbe.2020.07.004] [PMID: 36213693]
[13]
R.J. Oweis, and E.W. Abdulhay, "Seizure classification in EEG signals utilizing hilbert-huang transform", Biomed. Eng. Online, vol. 10, no. 1, p. 38, 2011.
[http://dx.doi.org/10.1186/1475-925X-10-38] [PMID: 21609459]
[14]
M. Sharma, R.B. Pachori, and U. Rajendra Acharya, "A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension", Pattern Recognit. Lett., vol. 94, pp. 172-179, 2017.
[http://dx.doi.org/10.1016/j.patrec.2017.03.023]
[15]
A. Guha, S. Ghosh, A. Roy, and S. Chatterjee, Epileptic seizure recognition using deep neural network Advances in intelligent systems and computing., vol. 937. Springer Verlag, 2020, pp. 21-28.
[16]
P. Nejedly, V. Kremen, V. Sladky, M. Nasseri, H. Guragain, P. Klimes, J. Cimbalnik, Y. Varatharajah, B.H. Brinkmann, and G.A. Worrell, "Deep-learning for seizure forecasting in canines with epilepsy", J. Neural Eng., vol. 16, no. 3, .036031
2019 [http://dx.doi.org/10.1088/1741-2552/ab172d] [PMID: 30959492]
[17]
B. Sun, J.J. Lv, L.G. Rui, Y.X. Yang, Y.G. Chen, C. Ma, and Z.K. Gao, "Seizure prediction in scalp EEG based channel attention dual-input convolutional neural network", Physica A, vol. 584, .126376
2021 [http://dx.doi.org/10.1016/j.physa.2021.126376]
[18]
Y. Zhang, Y. Guo, P. Yang, W. Chen, and B. Lo, "Epilepsy seizure prediction on EEG using common spatial pattern and convolutional neu-ral network", IEEE J. Biomed. Health Inform., vol. 24, no. 2, pp. 465-474, 2020.
[http://dx.doi.org/10.1109/JBHI.2019.2933046] [PMID: 31395568]
[19]
A.R. Ozcan, and S. Erturk, "Seizure prediction in scalp EEG using 3D convolutional neural networks with an image-based approach", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 27, no. 11, pp. 2284-2293, 2019.
[http://dx.doi.org/10.1109/TNSRE.2019.2943707] [PMID: 31562096]
[20]
A. Anandaraj, and P.J.A. Alphonse, "Tree based ensemble for enhanced prediction (TEEP) of epileptic seizures", Intell. Data Analy., vol. 26, no. 1, pp. 133-151, 2022.
[http://dx.doi.org/10.3233/IDA-205534]
[21]
S. Rukhsar, Y.U. Khan, O. Farooq, M. Sarfraz, and A.T. Khan, "Patient-specific epileptic seizure prediction in long-term scalp EEG signal using multivariate statistical process control", IRBM, vol. 40, no. 6, pp. 320-331, 2019.
[http://dx.doi.org/10.1016/j.irbm.2019.08.004]
[22]
K.M. Tsiouris, V.C. Pezoulas, M. Zervakis, S. Konitsiotis, D.D. Koutsouris, and D.I. Fotiadis, "A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals", Comput. Biol. Med., vol. 99, pp. 24-37, 2018.
[http://dx.doi.org/10.1016/j.compbiomed.2018.05.019] [PMID: 29807250]
[23]
B. Priya Prathaban, and R. Balasubramanian, "Dynamic learning framework for epileptic seizure prediction using sparsity based EEG reconstruction with optimized CNN classifier", Expert Syst. Appl., vol. 170, .114533
2021 [http://dx.doi.org/10.1016/j.eswa.2020.114533]
[24]
R. Jana, and I. Mukherjee, "Deep learning based efficient epileptic seizure prediction with EEG channel optimization", Biomed. Signal Process. Control, vol. 68, .102767
2021 [http://dx.doi.org/10.1016/j.bspc.2021.102767]
[25]
Y. Wang, J. Cao, X. Lai, and D. Hu, "Epileptic state classification for seizure prediction with wavelet packet features and random forest", Chinese Control and Decision Conference 2019pp., pp. 3983-3987, .
Nanchang, China [http://dx.doi.org/ 10.1109/CCDC.2019.8833249]
[26]
B. Kamousi, M. Hajinoroozi, S. Karunakaran, A. Grant, J. Yi, and R. Woo, "J. Parvizi X. Chao, "Systems and methods for seizure prediction and detection",", U.S. Patent 10743809B1, 2022.

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