E. B. Assi, D. K. Nguyen, S. Rihana, and M. Sawan, “Towards accurate prediction of epileptic seizures: A review,” Biomedical Signal Processing and Control, vol. 34, pp. 144–157, 2017.
A. K. Ngugi, C. Bottomley, I. Kleinschmidt, J. W. Sander, and C. R. Newton, “Estimation of the burden of active and life-time epilepsy: a meta-analytic approach,” Epilepsia, vol. 51, no. 5, pp. 883–890, 2010.
Article PubMed PubMed Central Google Scholar
Y.-P. Lin, C.-H. Wang, T.-P. Jung, T.-L. Wu, S.-K. Jeng, J.-R. Duann, and J.-H. Chen, “Eeg-based emotion recognition in music listening,” IEEE Transactions on Biomedical Engineering, vol. 57, no. 7, pp. 1798–1806, 2010.
B. Akbarian and A. Erfanian, “Automatic seizure detection based on nonlinear dynamical analysis of eeg signals and mutual information,” Basic and Clinical Neuroscience, vol. 9, no. 4, p. 227, 2018.
Article PubMed PubMed Central Google Scholar
D. Bhalla, B. Godet, M. Druet-Cabanac, and P.-M. Preux, “Etiologies of epilepsy: a comprehensive review,” Expert review of neurotherapeutics, vol. 11, no. 6, pp. 861–876, 2011.
K. Gadhoumi, J.-M. Lina, F. Mormann, and J. Gotman, “Seizure prediction for therapeutic devices: A review,” Journal of neuroscience methods, vol. 260, pp. 270–282, 2016.
S. Wiebe, M. Eliasziw, D. R. Bellhouse, and C. Fallahay, “Burden of epilepsy: the ontario health survey,” Canadian Journal of Neurological Sciences, vol. 26, no. 4, pp. 263–270, 1999.
J. F. Tellez-Zenteno, M. Pondal-Sordo, S. Matijevic, and S. Wiebe, “National and regional prevalence of self-reported epilepsy in canada,” Epilepsia, vol. 45, no. 12, pp. 1623–1629, 2004.
M. T. Salam, M. Mirzaei, M. S. Ly, D. K. Nguyen, and M. Sawan, “An implantable closedloop asynchronous drug delivery system for the treatment of refractory epilepsy,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 20, no. 4, pp. 432–442, 2012.
M. T. Salam, J. L. P. Velazquez, and R. Genov, “Seizure suppression efficacy of closed-loop versus open-loop deep brain stimulation in a rodent model of epilepsy,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 24, no. 6, pp. 710–719, 2015.
F. Mormann, R. G. Andrzejak, C. E. Elger, and K. Lehnertz, “Seizure prediction: the long and winding road,” Brain, vol. 130, no. 2, pp. 314–333, 2007.
C. Seiffert, T. M. Khoshgoftaar, J. Van Hulse, and A. Napolitano, “Rusboost: A hybrid approach to alleviating class imbalance,” IEEE transactions on systems, man, and cybernetics-part A: systems and humans, vol. 40, no. 1, pp. 185–197, 2009.
Y. Park, L. Luo, K. K. Parhi, and T. Netoff, “Seizure prediction with spectral power of eeg using cost-sensitive support vector machines,” Epilepsia, vol. 52, no. 10, pp. 1761–1770, 2011.
C. A. Teixeira, B. Direito, M. Bandarabadi, M. Le Van Quyen, M. Valderrama, B. Schelter, A. Schulze-Bonhage, V. Navarro, F. Sales, and A. Dourado, “Epileptic seizure predictors based on computational intelligence techniques: A comparative study with 278 patients,” Computer methods and programs in biomedicine, vol. 114, no. 3, pp. 324–336, 2014.
Y. Li, X.-D. Wang, M.-L. Luo, K. Li, X.-F. Yang, and Q. Guo, “Epileptic seizure classification of eegs using time–frequency analysis based multiscale radial basis functions,” IEEE journal of biomedical and health informatics, vol. 22, no. 2, pp. 386–397, 2017.
P. Agarwal, H.-C. Wang, and K. Srinivasan, “Epileptic seizure prediction over eeg data using hybrid cnn-svm model with edge computing services,” in MATEC web of conferences, vol. 210, p. 03016, EDP Sciences, 2018.
B. Behnoush, E. Bazmi, S. Nazari, S. Khodakarim, M. Looha, and H. Soori, “Machine learning algorithms to predict seizure due to acute tramadol poisoning,” Human & Experimental Toxicology, vol. 40, no. 8, pp. 1225–1233, 2021.
N. Mahmoodian, A. Boese, M. Friebe, and J. Haddadnia, “Epileptic seizure detection using cross-bispectrum of electroencephalogram signal,” seizure, vol. 66, pp. 4–11, 2019.
A. Holzinger, “Introduction to machine learning & knowledge extraction (make),” 2019.
F. George, A. Joseph, B. Baby, A. John, T. John, M. Deepak, G. Nithin, and P. Sathidevi, “Epileptic seizure prediction using eeg images,” in 2020 International Conference on Communication and Signal Processing (ICCSP), pp. 1595–1598, IEEE, 2020.
K. Singh and J. Malhotra, “Two-layer lstm network-based prediction of epileptic seizures using eeg spectral features,” Complex & Intelligent Systems, vol. 8, no. 3, pp. 2405–2418, 2022.
J. Guttag, “Chb-mit scalp eeg database (version 1.0. 0),” PhysioNet, 2010.
R. W. Homan, J. Herman, and P. Purdy, “Cerebral location of international 10–20 system electrode placement,” Electroencephalography and clinical neurophysiology, vol. 66, no. 4, pp. 376–382, 1987.
Article CAS PubMed Google Scholar
H. Shokouh Alaei, M. A. Khalilzadeh, and A. Gorji, “Optimal selection of sop and sph using fuzzy inference system for on-line epileptic seizure prediction based on eeg phase synchronization,” Australasian physical & engineering sciences in medicine, vol. 42, pp. 1049–1068, 2019.
J. Yan, J. Li, H. Xu, Y. Yu, and T. Xu, “Seizure prediction based on transformer using scalp electroencephalogram,” Applied Sciences, vol. 12, no. 9, p. 4158, 2022.
T. Maiwald, M. Winterhalder, R. Aschenbrenner-Scheibe, H. U. Voss, A. Schulze-Bonhage, and J. Timmer, “Comparison of three nonlinear seizure prediction methods by means of the seizure prediction characteristic,” Physica D: nonlinear phenomena, vol. 194, no. 3-4, pp. 357–368, 2004.
N. D. Truong, A. D. Nguyen, L. Kuhlmann, M. R. Bonyadi, J. Yang, S. Ippolito, and O. Kavehei, “Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram,” Neural Networks, vol. 105, pp. 104–111, 2018.
Y. Zheng, G. Wang, K. Li, G. Bao, and J. Wang, “Epileptic seizure prediction using phase synchronization based on bivariate empirical mode decomposition,” Clinical Neurophysiology, vol. 125, no. 6, pp. 1104–1111, 2014.
D. Griffin and J. Lim, “Signal estimation from modified short-time fourier transform,” IEEE Transactions on acoustics, speech, and signal processing, vol. 32, no. 2, pp. 236–243, 1984.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 7132–7141, 2018.
A. Shankar, S. Dandapat, and S. Barma, “Seizure types classification by generating input images with in-depth features from decomposed eeg signals for deep learning pipeline,” IEEE Journal of Biomedical and Health Informatics, vol. 26, no. 10, pp. 4903–4912, 2022.
A. Agarap, “Deep learning using rectified linear units (relu),” arXiv:1803.08375, 2018.
S.-H. Gao, M.-M. Cheng, K. Zhao, X.-Y. Zhang, M.-H. Yang, and P. Torr, “Res2net: A new multi-scale backbone architecture,” IEEE transactions on pattern analysis and machine intelligence, vol. 43, no. 2, pp. 652–662, 2019.
N. Computation, “Long short-term memory,” Neural Comput, vol. 9, pp. 1735–1780, 2016.
Z. Huang, W. Xu, and K. Yu, “Bidirectional lstm-crf models for sequence tagging. arxiv,” arXiv:1508.01991, 2015.
C. Armon, R. A. Radtke, A. H. Friedman, and D. V. Dawson, “Predictors of outcome of epilepsy surgery: multivariate analysis with validation,” Epilepsia, vol. 37, no. 9, pp. 814–821, 1996.
Article CAS PubMed Google Scholar
G. Choi, C. Park, J. Kim, K. Cho, T.-J. Kim, H. Bae, K. Min, K.-Y. Jung, and J. Chong, “A novel multi-scale 3d cnn with deep neural network for epileptic seizure detection,” in 2019 IEEE International Conference on Consumer Electronics (ICCE), pp. 1–2, IEEE, 2019.
M. S. Nafea and Z. H. Ismail, “Supervised machine learning and deep learning techniques for epileptic seizure recognition using eeg signals—a systematic literature review,” Bioengineering, vol. 9, no. 12, p. 781, 2022.
Article PubMed PubMed Central Google Scholar
L. Deng, G. Li, S. Han, L. Shi, and Y. Xie, “Model compression and hardware acceleration for neural networks: A comprehensive survey,” Proceedings of the IEEE, vol. 108, no. 4, pp. 485–532, 2020.
N. Moghim and D. W. Corne, “Predicting epileptic seizures in advance,” PloS one, vol. 9, no. 6, p. e99334, 2014.
M.-P. Hosseini, D. Pompili, K. Elisevich, and H. Soltanian-Zadeh, “Optimized deep learning for eeg big data and seizure prediction bci via internet of things,” IEEE Transactions on Big Data, vol. 3, no. 4, pp. 392–404, 2017.
Y. Wang, J. Cao, X. Lai, and D. Hu, “Epileptic state classification for seizure prediction with wavelet packet features and random forest,” in 2019 Chinese Control And Decision Conference (CCDC), pp. 3983–3987, IEEE, 2019.
S. M. Usman, S. Khalid, and M. H. Aslam, “Epileptic seizures prediction using deep learning techniques,” Ieee Access, vol. 8, pp. 39998–40007, 2020.
H. Daoud, P. Williams, and M. Bayoumi, “Iot based efficient epileptic seizure prediction system using deep learning,” in 2020 IEEE 6th World Forum on Internet of Things (WF-IoT), pp. 1–6, IEEE, 2020.
S. Shi and W. Liu, “B2-vit net: Broad vision transformer network with broad attention for seizure prediction,” IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 32, pp. 178–188, 2023.
H. Ma, Y. Wu, Y. Tang, R. Chen, T. Xu, and W. Zhang, “Parallel dual-branch fusion network for epileptic seizure prediction,” Computers in Biology and Medicine, vol. 176, p. 108565, 2024.
P. Detti, G. Vatti, and G. Zabalo Manrique de Lara, “Eeg synchronization analysis for seizure prediction: A study on data of noninvasive recordings,” Processes, vol. 8, no. 7, p. 846, 2020.
V. Shah, E. Von Weltin, S. Lopez, J. R. McHugh, L. Veloso, M. Golmohammadi, I. Obeid, and J. Picone, “The temple university hospital seizure detection corpus,” Frontiers in neuroinformatics, vol. 12, p. 83, 2018.
Comments (0)