Abstract
An epileptic seizure is a disease of the central nervous system caused by abnormal activity generated by neurons in the brain. Seizure reduces the quality of life of epileptic patients due to unconsciousness. In this paper, an efficient seizure prediction system is proposed to improve the quality of life. The raw EEG signal is converted into the EEG signal image. Then, a convolutional neural network is used for training the prediction system. The performance of the proposed system is evaluated using the CHB-MIT dataset. The classification accuracy of interictal and preictal states is achieved up to 94.33% using 10-fold cross-validation. Due to the presence of noise in the EEG signal, a pool based technique is used to make the decision on the majority of a 1 min EEG signal that increase the accuracy of the prediction of upcoming seizures.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Epilepsy Foundations, http://www.epilepsy.com
C.L. Deckers, P. Genton, G.J. Sills, D. Schmidt, Current limitations of antiepileptic drug therapy: a conference review. Epilepsy Res. 53, 1–17 (2013)
M.Z. Parvez, M. Paul, Epileptic seizure detection by analyzing EEG signals using different transformation techniques. Neurocomputing 145, 190–200 (2014)
S. Elgohary, S. Eldawlatly, M.I. Khalil, Epileptic seizure prediction using zero-crossings analysis of EEG wavelet detail coefficients, in IEEE Conference on Computational Intelligence in Bioinformatics and Computational Biology, pp. 1–6 (2016)
A.V. Esbroeck, L. Smith, Z. Syed, S. Singh, Z. Karam, Multi-task seizure detection: addressing intra-patient variation in seizure morphologies. Mach. Learn. 102(3), 309–321 (2016)
J. Liang, R. Lu, C. Zhang, F. Wang, Predicting seizures from electroencephalography recordings: a knowledge transfer strategy, in IEEE International Conference on Healthcare Informatics, pp. 184–191 (2016)
Y. Park, L. Luo, K. Parhi, T. Netoff, Seizure prediction with spectral power of EEG using cost-sensitive support vector machines. Epilepsia 52(10), 1761–1770 (2011)
J.R. Williamson, D.W. Bliss, D.W. Browne, J.T. Narayanan, Seizure prediction using EEG spatiotemporal correlation structure. Epilepsy Behav. 25(2), 230–238 (2012)
J. Xiang, C. Li, H. Li, R. Cao, B. Wang, X. Han, J. Chen, The detection of epileptic seizure signals based on fuzzy entropy. J. Neurosci. Methods 243, 18–25 (2015)
T.N. Alotaiby, S.A. Alshebeili, F.M. Alotaibi, S.R. Alrshoud, Epileptic seizure prediction using CSP and LDA for scalp EEG signals. Comput. Intell. Neurosci. 2017, 1–11 (2017)
L.A.S. Kitano, M.A.A. Sousa, S.D. Santos, R. Pires, S. Thome-Souza, A.B. Campo, Epileptic seizure prediction from EEG signals using unsupervised learning and a polling-based decision process, pp. 117–126 (2018)
H. Khan, L. Marcuse, M. Fields, K. Swann, B. Yener, Focal onset seizure prediction using convolutional networks. IEEE Trans. Biomed. Eng. 65(9), 2109–2118 (2018)
N.D. Truong, A.D. Nguyen, L. Kuhlmann, M.R. Bonyadi, J. Yang, S. Ippolito, O. Kavehei, Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw. 105, 104–111 (2018)
Y. Lecun, Y. Bengio, Convolutional networks for images, speech, and time-series (1995)
CHB-MIT Scalp EEG Database, https://physionet.org/content/chbmit/1.0.0/
A. Shoeb, Application of machine learning to epileptic seizure onset detection and treatment, Ph.D. Thesis, Massachusetts institute of technology (2009)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Singapore Pte Ltd.
About this chapter
Cite this chapter
Jana, R., Bhattacharyya, S., Das, S. (2020). Patient-Specific Seizure Prediction Using the Convolutional Neural Networks. In: Bhattacharyya, S., Mitra, S., Dutta, P. (eds) Intelligence Enabled Research. Advances in Intelligent Systems and Computing, vol 1109. Springer, Singapore. https://doi.org/10.1007/978-981-15-2021-1_7
Download citation
DOI: https://doi.org/10.1007/978-981-15-2021-1_7
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-2020-4
Online ISBN: 978-981-15-2021-1
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)