An Enhanced Automated Epileptic Seizure Detection Using ANFIS, FFA and EPSO Algorithms

Article Sidebar

Model of 2 input one output ANFIS Network
PDF
Published: Apr 3, 2023
DOI: https://doi.org/10.17762/ijritcc.v11i4s.6307
Keywords:
Epileptic Seizure, EEG signal, FFA, EPSO, ANFIS

Main Article Content

Sumant Kumar Mohapatra
Department of Computer Science and Engineering, Institute of Technical Education and Research Siksha ‘O’ Anusandhan (Deemed to be) University, Odisha, India
Srikanta Patnaik
Department of Computer Science and Engineering, Institute of Technical Education and Research Siksha ‘O’ Anusandhan (Deemed to be) University, Odisha, India
Sumant Kumar Mohapatra
Department of Computer Science and Engineering, Institute of Technical Education and Research Siksha ‘O’ Anusandhan (Deemed to be) University, Odisha, India

Abstract

Objectives: Electroencephalogram (EEG) signal   gives   a   viable perception about the neurological action of the human brain that aids the detection of epilepsy. The objective of this study is to build an accurate automated hybrid model for epileptic seizure detection. Methods: This work develops a computer-aided diagnosis (CAD) machine learning model which can spontaneously classify pre-ictal and ictal EEG signals. In the proposed method two most effective nature inspired algorithms, Firefly algorithm (FFA) and Efficient Particle Swarm Optimization (EPSO) are used to determine the optimum parameters of Adaptive Neuro Fuzzy Inference System (ANFIS) network. Results: Compared to the FFA and EPSO algorithm separately, the composite (ANFIS+FFA+EPSO) optimization algorithm outperforms in all respects. The proposed technique achieved accuracy, specificity, and sensitivity of 99.87%, 98.71% and 100% respectively. Conclusion: The ANFIS-FFA-EPSO method is able to enhance the seizure detection outcomes for demand forecast in hospital.

Article Details

How to Cite
Mohapatra, S. K. ., Patnaik, S. ., & Kumar Mohapatra, S. . (2023). An Enhanced Automated Epileptic Seizure Detection Using ANFIS, FFA and EPSO Algorithms . International Journal on Recent and Innovation Trends in Computing and Communication, 11(4s), 57–67. https://doi.org/10.17762/ijritcc.v11i4s.6307
Issue
Vol. 11 No. 4s (2023)
Section
Articles

References

WHO, World Health Organization: 2001 Epilepsy: Epidemiology, Aetiology And Prognosis, WHO Factsheet.

World Health Organization, 2017. Epilepsy. http://www.who.int/mediacentre/factsheets/ fs 999/ en/.

American Epilepsy Society, Facts and Figures. https://www.aesnet.org/for_patients/facts_Figures.

Harvard Health Publications, Harvard Medical School, 2014. Seizure overview. http://www.health.harvard. Edu/mind-and-mood/ seizure-overview.

International League against Epilepsy (ILAE), https://www.epilepsydiagnosis.org/.

Nihal Fatma Güler, Elif Derya Übeyli, ?nan Güler (2005) recurrent neural networks employing Lyapunov exponents for EEG signal classification. Expert System with Applications.25: 506-514.

P. J. G. Lisboa, A. Vellido,, H. Wong, (2000) Outstanding Issues for Clinical Decision Support with Neural Networks. J. Artificial Neural Networks in Medicine and Biology, Springer. 63- 71.

Georege J. Tsekuoras, John Koukoulis, Nikos E. Mastorakis, (2010). An optimize neural network for predicting settlement during tunneling excavation. WSEAS transaction on system. Issue 12. 9: 1153-1167.

Adeli, H., Zhou, Z., Dadmehr, N., 2003. Analysis of EEG records in an epileptic patient using wavelet transform, Journal of Neuroscience Methods, 123(1):69-87.

Acharya, U. R, Sree, S. V., Swapna, G., Martis, R. J, Suri, J. S., 2013. Automated EEG analysis of epilepsy: A review, Knowledge-Based Systems, 45:147-165.

Faust, O., Acharya, U. R., Adeli, H., Adeli, A., 2015. Wavelet-based EEG processing for computeraided seizure detection and epilepsy diagnosis, Seizure 26:56-64.

Acharya, U. R, Fujita, H., Sudarshan, V. K., Bhat, S., Koh, J. E. W., 2015. Application of entropies for automated diagnosis of epilepsy using EEG signals: A review, Knowledge- Based Systems, 88:85-96.

Chua, K. C., Chandran, V., Acharya, R., & Lim, C. M. (2008). Automatic identification of epilepsy by hos and power SPECIFICITYctrum parameters using eeg signals: A comparative study. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (pp. 3824–3827). doi:10.1109/IEMBS.2008.4650043

Chua, K. C., Chandran, V., Acharya, U. R., & Lim, C. M. (2011). Application of higher order SPECIFICITYctra to identify epileptic eeg. Journal of Medical Systems,35, 1563–1571.

Acharya, U. R., Sree, S. V., & Suri, J. S. (2011a). Automatic detection of epileptic eeg signals using higher order cumulant features. International Journal of Neural Systems, 21, 403–414.

Martis, R. J., Acharya, U. R., Tan, J.-H., Petznick, A., Tong, L., Chua, C. K., & Ng, E. Y. K. (2013). Application of intrinsic time-scale decomposition (itd) to eeg signals for automated seizure prediction. International journal of neural systems, 23 5,1350023.

Kannathal, N., Lim, C., Acharya, U., & Sadasivan, P. (2005). Entropies for detection of epilepsy in eeg. Comput. Methods Programs Biomed., 80, 187– 194.

Acharya, U., Molinari, F., Vinitha Sree, S., Chattopadhyay, S., Kwan-Hoong, N.,& Suri, J. (2012a). Automated diagnosis of epileptic eeg using entropies. Biomed. Signal Process Control, 7, 401–408.

Guler, I., & Ubeyli, E. (2007). Multiclass support vector machines for eeg-signals classification. IEEE Trans. Inf. Technol. Biomed., 11, 117–126.

Ghosh-Dastidar, S., Adeli, H., 2007, Improved spiking neural networks for EEG classification and epilepsy and seizure detection, Integrated Computer-Aided Engineering, 14(3):187-212.

Ghosh-Dastidar S., Adeli, H., 2009. A new supervised learning algorithm for multiple spiking neural networks with application in epilepsy and seizure detection, Neural Networks, 22:1419- 1431

Ghosh-Dastidar, S., Adeli, H., Dadmehr, N., 2007. Mixed-band wavelet-chaos- neural network methodology for epilepsy and epileptic seizure detection, IEEE Transactions on Biomedical Engineering, 54(9):1545-1551.

Ghosh-Dastidar, S. and Adeli, H. 2009a, Spiking Neural Networks, International Journal of Neural Systems 19(4):295-308.

Acharya, U. R, Chua, K. C., Lim, T. C., Dorithy, Suri, J. S., 2009. Automatic identification of epileptic EEG signals using nonlinear parameters, Journal of Mechanics in Medicine and Biology,

Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M., 2009. Automatic identification of epileptic electroencephalography signals using higher-order SPECIFICITYctra, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine, 223(4):485-495.

Chua, K. C., Chandran, V., Acharya, U. R., Lim, C. M., 2010. Application of higher order SPECIFICITYctra to identify epileptic EEG, Journal of Medical Systems,35(6):1563-1571.

Faust, O., Acharya, U. R., Lim, C. M., Sputh, B. H., 2010. Automatic identification of epileptic and background EEG signals using frequency domain parameters, International Journal of Neural Systems 20(2):159-176

Acharya, U. R, Sree, S. V., Suri, J. S., 2011a. Automatic detection of epileptic EEG signals using higher order cumulants features, International Journal of Neural Systems,21(5):403-414.

Acharya, U. R, Sree, S. V., Chattopadhyay, S., Yu, W. W., Ang, P. C. A., 2011b. Application of recurrence quantification analysis for the automated identification of epileptic EEG signals, International Journal of Neural Systems, 21(3):199-211.

Guo, L., Rivero, D., Dorado, J., Munteanu, C. R., Pazos, A., 2011. Automatic feature extraction using genetic programming: An application to epileptic EEG classification, Expert Systems with Applications, 38:10425-10436.

Acharya, U. R, Sree, S. V., Ang, P. C. A., Yanti, R., Suri, J. S., 2012a. Application of non-linear and wavelet based features for the automated identification of epileptic EEG signals, International Journal of Neural Systems, 22(2):1250002-1-1250002-14.

Acharya, U. R, Molinari, F., Sree, S. V., Chattopadhay, S., Ng, K. H., 2012b. Automated diagnosis of epileptic EEG using entropies, Biomedical Signal Processing Control, 7(4):410-408.

Acharya, U. R, Sree, S. V., Ang, P. C. A., Suri, J. S., 2012c. Use of principal component analysis for automatic detection of epileptic EEG activities, Expert Systems with applications, 39(10):9072- 9078.

Acharya, U. R, Yanti, R., Swapna, G., Sree, V. S., Martis, R. J., Suri, J. S., 2012d. Automated diagnosis of epileptic electroencephalogram using independent component analysis and discrete wavelet transform for different electroencephalogram durations, Proceedings of the Institution of Mechanical Engineers. Part H, Journal of Engineering in Medicine, 227(3).

Martis. R. J., Acharya. U. R., Tan. J. H., Petznick. A., Yanti. R., Chua. K. C., Ng. E. Y. K., Tong. L., 2012. Application of empirical mode decomposition (EMD) for automated detection of epilepsy using EEG signals, International Journal of Neural Systems, 22(6):1250027-1-1250027-16.

Bhattacharyya, A., Pachori, R. B., 2017a, A multivariate approach for patient- SPECIFICITYcific EEG seizure detection using empirical wavelet transform, IEEE Transactions on Biomedical Engineering, 64(9):2003-2015.

Bhattacharyya, A., Pachori, R. B., Upadhyay, A., Acharya, U. R., 2017b, Tunable- Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals, Application of Signal Processing Methods for Systematic Analysis of Physiological Health, 7(4):385.

Sharma, M., Pachori, R. B., Acharya, U. R., 2017. A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension, Pattern Recognition Letters, 94:172-179.

U. Rajendra Acharya, Shu Lih Oh , Yuki Hagiwara , Jen Hong Tan , Hojjat Adeli , Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals, Computers in Biology and Medicine, S0010-4825(17)30315-3.

Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., David, P., Elger, C. E.,2001. Indications of Non-linear deterministic and finite dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state, Physical Review E, 64:061907.

Ebtehaj, I., Bonakdari, H., 2014. Performance Evaluation of Adaptive Neural Fuzzy Inference System for Sediment Transport in Sewers. Water Resour. Manag. 28, 4765–4779. doi:10.1007/s11269-014-0774-0.

Takagi, T., Sugeno, M., 1985. Fuzzy Identification of Systems and Its Applications to Modeling and Control. IEEE Trans. Syst. Man Cybern. SMC-15, 116–132. doi:10.1109/TSMC.1985.6313399.

Chang, F.J., Chang, Y.T., 2006. Adaptive neuro-fuzzy inference system for prediction of water level in reservoir. Adv. Water Resour. 29, 1–10. doi:10.1016/j.advwatres.2005.04.015

Iztok Fister Jr, Matjaz Perc, Salahuddin M. Kamal, Iztok Fister.” A review of chaos- based firefly algorithms: Perspectives and research challenges”, Applied Mathematics and Computation 252 (2015) 155–165.

Iztok Fister a,n , Iztok Fister Jr.a , Xin-She Yang b , Janez Brest,” A comprehensive review of firefly algorithms”, Swarm and Evolutionary Computation.

Kennedy J, Eberhart RC. Particle swarm optimization. In: Proceedings of IEEE international conference on neural networks IV:1995; 1942–1948.

Qasem SN, Shamsuddin SM. Hybrid learning enhancement of RBF network based on particle swarm optimization; 5553; 2009. p. 19–29.

Dehuri S, Roy R, Cho SB, Ghosh A. An improved swarm optimized functional link artificial neural

Duda, R. O., Hart, P. E., Stork, D. G., 2001. Pattern classification 2nd edition, NewYork, John Wiley and Sons.