A Lightweight Deep Learning Model for The Early Detection of Epilepsy

Main Article Content

Saranya, D. Karthika Renuka, M. Sivakumar, L. Ashok Kumar

Abstract

Epilepsy is a neurological disorder and non communicable disease which affects patient's health, During this seizure occurrence normal brain function activity will be interrupted. It may happen anywhere and anytime so it leads to very dangerous problems like sudden unexpected death. Worldwide seizure affected people are around 65% million. So it must be considered as serious problem for the early prediction.  A number of different types of screening tests will be conducted to assess the severity of the symptoms such as EEG,MRI, ECG, and ECG. There are several reasons why EEG signals are used, including their affordability, portability, and ability to display. The proposed model used bench-marked CHB-MIT EEG datasets for the implementation of early prediction of epilepsy ensures its seriousness and leads to perfect diagnosis. Researchers proposed Various ML /DL methods to  try for the early prediction of epilepsy but still it has some challenges in terms of efficiency and precision Seizure detection techniques typically employ the use of convolutional neural networks (CNN) and a bidirectional short- and long-term memory (Bi-LSTM) model in the realm of deep learning. This method leverages the strengths of both models to effectively analyze electroencephalogram (EEG) data and detect seizure patterns. These light weight models have been found to be effective in automatically detecting seizures in deep learning techniques with an accuracy rate of up to 96.87%. Hence, this system has the potential to be utilized for categorizing other types of physiological signals too, but additional research is required to confirm this.

Article Details

How to Cite
Saranya, et al. (2023). A Lightweight Deep Learning Model for The Early Detection of Epilepsy. International Journal on Recent and Innovation Trends in Computing and Communication, 11(10), 397–405. https://doi.org/10.17762/ijritcc.v11i10.8504
Section
Articles
Author Biography

Saranya, D. Karthika Renuka, M. Sivakumar, L. Ashok Kumar

Saranya1, D. Karthika Renuka2, M. Sivakumar3, L. Ashok Kumar4

1Assistant Professor, Dept of CSE, Dr.N.G.P.Institute of Technology, Coimbatore,Tamil Nadu,India.

saranyasasini@gmail.com

2Associate Professor, Dept of IT, PSG College of Technology, Coimbatore, Tamil Nadu,India.

dkr.it@psgtech.ac.in

3Professor, Dept of Data Science, Saveetha institute of Medical and Technical Science, Chennai, Tamil Nadu,India

siva.recursion@gmail.com

4Professor, Dept of EEE, PSG College of Technology, Coimbatore, Tamil Nadu,India.

lak.eee@psgtech.ac.in

References

] Beeraka, S.M., Kumar, A., Sameer, M. et al. Accuracy Enhancement of Epileptic Seizure Detection: A Deep Learning Approach with Hardware Realization of STFT. Circuits Syst Signal Process 41, 461–484 (2022). https://doi.org/10.1007/s00034-021-01789-4

] Singh, K., Malhotra, J. Two-layer LSTM network-based prediction of epileptic seizures using EEG spectral features. Complex Intell. Syst. 8, 2405–2418 (2022)./doi.org/10.1007/s40747-021-00627-z

] Adeli, S. Ghosh-Dastidar and N. Dadmehr, "A Wavelet-Chaos Methodology for Analysis of EEGs and EEG Subbands to Detect Seizure and Epilepsy," in IEEE Transactions on Biomedical Engineering, vol. 54, no. 2, pp. 205-211, Feb. 2007, doi: 10.1109/TBME.2006.886855.

] Chandler, J. Bisasky, J.L.V.M. Stanislaus, T. Mohsenin, Real-time multi-channel seizure detection and analysis hardware, in IEEE Biomedical Circuits and Systems Conference (BioCAS), San Diego, CA, USA (2011), pp. 41–44. https://doi.org/10.1109/BioCAS.2011.6107722

] Acharya et al. "Seizure Detection Using Deep Learning Models With Multi-Channel EEG Signals" 2018 International Conference on Electronics, Information, and Communication (ICEIC), Honolulu, HI, USA, 2018, pp. 1-5, doi: 10.23919/ELINFOCOM.2018.8330671.

] Wei, X., Zhou, L., Chen, Z. et al. Automatic seizure detection using three-dimensional CNN based on multi-channel EEG. BMC Med Inform Decis Mak 18 (Suppl 5), 111 (2018). https://doi.org/10.1186/s12911-018-0693-8

] Y. Yuan et al., "A novel channel-aware attention framework for multi-channel EEG seizure detection via multi-view deep learning," 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI), Las Vegas, NV, USA, 2018, pp. 206-209, doi: 10.1109/BHI.2018.8333405.

] Cheng, S. He, V. Stojanovic, X. Luan, F. Liu, Fuzzy fault detection for Markov jump systems with partly accessible hidden information: an event-triggered approach. IEEE Trans. Cybern. (2021). https://doi.org/10.1109/TCYB.2021.3050209 .

] McSharry, P., Smith, L. & Tarassenko, L. Prediction of epileptic seizures: are nonlinear methods relevant?. Nat Med 9, 241–242 (2003). https://doi.org/10.1038/nm0303-241

] Tao, J. Li, Y. Chen, V. Stojanovic, H. Yang, Robust point-to-point iterative learning control with trial-varying initial conditions. IET Control Theory Appl. 14(19), 3344–3350 (2020). https://doi.org/10.1049/iet-cta.2020.0557

] Sokolova, M., and Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Inform. Process. Manag. 45, 427–437. doi: 10.1016/j.ipm.2009.03.002

] Wang, D., Ren, D., Li, K., Feng, Y., Ma, D., Yan, X., et al. (2018). Epileptic seizure detection in long-term EEG recordings by using wavelet-based directed transfer function. IEEE Transact. Biomed. Eng. 65, 2591–2599. doi: 10.1109/tbme.2018.2809798

] Wang, X., Zhao, Y., and Pourpanah, F. (2020). Recent advances in deep learning. Int. J. Mach. Learn. Cybern. 11, 747–750. doi: 10.1007/s13042-020-01096-5

] Wei, X., Zhou, L., Zhang, Z., Chen, Z., and Zhou, Y. (2019). Early prediction of epileptic seizures using a long-term recurrent convolutional network. J. Neurosci. Methods 327:108395. doi: 10.1016/j.jneumeth.2019.108395

] Yavuz, E., Kasapba??, M. C., Eyüpo?lu, C., and Yaz?c?, R. (2018). An epileptic seizure detection system based on cepstral analysis and generalized regression neural network. Biocybern. Biomed. Eng. 38, 201–216. doi: 10.1016/j.bbe.2018.01.002

] 12.Gramacki, A., Gramacki, J. A deep learning framework for epileptic seizure detection based on neonatal EEG signals. Sci Rep 12, 13010 (2022). https://doi.org/10.1038/s41598-022-15830-2.

] Yuan, Y., Xun, G., Jia, K., and 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, NY. doi: 10.1145/3107411.3107419

] Zhou, M., Tian, C., Cao, R., Wang, B., Niu, Y., Hu, T., et al. (2018). Epileptic Seizure detection based on EEG signals and CNN. Fronti. Neuroinform. 12:95. doi: 10.3389/fninf.2018.00095

] Ma et al., "A Multi-Channel Feature Fusion CNN-Bi-LSTM Epilepsy EEG Classification and Prediction Model Based on Attention Mechanism," in IEEE Access, vol. 11, pp. 62855-62864, 2023, doi: 10.1109/ACCESS.2023.3287927.

] 15.A. M. Abdelhameed, H. G. Daoud and M. Bayoumi, "Deep Convolutional Bidirectional LSTM Recurrent Neural Network for Epileptic Seizure Detection," 2018 16th IEEE International New Circuits and Systems Conference (NEWCAS), Montreal, QC, Canada, 2018, pp. 139-143, doi: 10.1109/NEWCAS.2018.8585542.

] Lu, A. Wen, L. Sun, H. Wang, Y. Guo and Y. Ren, "An Epileptic Seizure Prediction Method Based on CBAM-3D CNN-LSTM Model," in IEEE Journal of Translational Engineering in Health and Medicine, vol. 11, pp. 417-423, 2023, doi: 10.1109/JTEHM.2023.3290036.

] Rogowski, Z., Gath, I. & Bental, E. On the prediction of epileptic seizures. Biol. Cybern. 42, 9–15 (1981). https://doi.org/10.1007/BF00335153

] Salant, Y., Gath, I. & Henriksen, O. Prediction of epileptic seizures from two-channel EEG. Med. Biol. Eng. Comput. 36, 549–556 (1998). https://doi.org/10.1007/BF02524422

] Raghu, S., Sriraam, N., Vasudeva Rao, S. et al. Automated detection of epileptic seizures using successive decomposition index and support vector machine classifier in long-term EEG. Neural Comput & Applic 32, 8965–8984 (2020). https://doi.org/10.1007/s00521-019-04389-1

] Yuan and D. Wei, "A seizure prediction method based on efficient features and BLDA," 2015 IEEE International Conference on Digital Signal Processing (DSP), Singapore, 2015, pp. 177-181, doi: 10.1109/ICDSP.2015.7251854.

] Zhang, S., Chen, D., Ranjan, R. et al. A lightweight solution to epileptic seizure prediction based on EEG synchronization measurement. J Supercomput 77, 3914–3932 (2021). https://doi.org/10.1007/s11227-020-03426-4

] R. Ozcan and S. Erturk, "Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach," in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 27, no. 11, pp. 2284-2293, Nov. 2019, doi: 10.1109/TNSRE.2019.2943707.

] M. Abdelhameed and M. Bayoumi, "Semi-Supervised Deep Learning System for Epileptic Seizures Onset Prediction," 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), Orlando, FL, USA, 2018, pp. 1186-1191, doi: 10.1109/ICMLA.2018.00191.

] Shahbazi and H. Aghajan, "A GENERALIZABLE MODEL FOR SEIZURE PREDICTION BASED ON DEEP LEARNING USING CNN-LSTM ARCHITECTURE," 2018 IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, 2018, pp. 469-473, doi: 10.1109/GlobalSIP.2018.8646505.

] M. Varnosfaderani et al., "A Two-Layer LSTM Deep Learning Model for Epileptic Seizure Prediction," 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), Washington DC, DC, USA, 2021, pp. 1-4, doi: 10.1109/AICAS51828.2021.9458539.

] Freestone, Dean R.a,?; Karoly, Philippa J.a,b,c,?; Cook, Mark J.a. A forward-looking review of seizure prediction. Current Opinion in Neurology 30(2):p 167-173, April 2017. | DOI: 10.1097/WCO.0000000000000429

] Liu, J. Li and M. Shu, "Epileptic Seizure Prediction Based on Region Correlation of EEG Signal," 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems (CBMS), Rochester, MN, USA, 2020, pp. 120-125, doi: 10.1109/CBMS49503.2020.00030.

] Wang, J. Yang and M. Sawan, "A Novel Multi-scale Dilated 3D CNN for Epileptic Seizure Prediction," 2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS), Washington DC, DC, USA, 2021, pp. 1-4, doi: 10.1109/AICAS51828.2021.9458571.

] Sakkos., Liu, H., Han, J. et al. End-to-end video background subtraction with 3d convolutional neural networks. Multimed Tools Appl 77, 23023–23041 (2018). https://doi.org/10.1007/s11042-017-5460-9