Abstract
Epilepsy seizures are sudden, chaotic neurological functions. The complexity of the brain is revealed via electroencephalography (EEG). Visual examination-based EEG signal analysis is time-consuming, expensive, and difficult. Epilepsy-related mortality is a serious concern. In the diagnostic procedure, computer-assisted diagnosis approaches for precise and automatic detection and classification of epileptic seizures play a crucial role. Due to the classifier's high processing time requirements caused by its mathematical complexity and computational time, we propose a hybrid water cycle algorithm (WCA)–particle swarm optimization (PSO) optimized ensemble extreme learning machine (EELM) classification of seizures to improve the classification performance of the classifier. Firstly, we use feature extraction by utilizing the wavelet transform. The extracted features are aligned as input to the WCA–PSO–EELM for classification. The particle swarm optimization (PSO) algorithm is used to initialize the optimization variables of a WCA algorithm, and the WCA algorithm is used to optimize the input weight of the ELM (i.e., the WCA–PSO–ELM (WPELM)) for classification of seizure and non-seizure EEG signals. University of Bonn database is used for the experiment. The performance measures sensitivity, specificity, and accuracy are considered and achieved 98.78%, 99.23%, and 99.12%, that is, higher than those of other conventional algorithms. To validate the robustness of the WCA–PSO algorithm, three benchmark functions are considered for optimization. The comparison results are presented to visualize the uniqueness of the proposed WCA–PSO–EELM classifier. From the comparison results, it was observed that the proposed WCA–PSO–EELM model outperformed in classifying the seizure and non-seizure EEG signals.
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In this study, EEG signals are collected from University of Bonn, Germany.
References
World Health Organization. Epilepsy;. http://www.who.int/mediacentre/ factsheets/ fs999/en/.
https://medalerthelp.org/blog/epilepsy-statistics/ February 21, 2021, By Dr. Nikola Djordjevic, MD.
Abbasi R, Esmaeilpour M (2017) Selecting statistical characteristics of brain signals to detect epileptic seizures using discrete wavelet transform and perceptron neural network. IJIMAI 4(5):33–38
Sriraam N, Raghu S, Tamanna K (2018) Automated epileptic seizures detection using multi-features and multilayer perceptron neural network. Brain Inf. 5:10. https://doi.org/10.1186/s40708-018-0088-8
Yuan S, Zhou W, Chen L (2018) Epileptic seizure prediction using diffusion distance and bayesian linear discriminate analysis on intracranial EEG. Int J Neural Syst 28(1):1750043. https://doi.org/10.1142/S0129065717500435
Gupta V, Pachori RB (2019) Epileptic seizure identification using entropy of FBSE based EEG rhythms. Biomed Signal Process Control 53:101569. ISSN 1746-8094. https://doi.org/10.1016/j.bspc.2019.101569
Sharma R, Pachori RB, Sircar P (2020) Seizures classification based on higher order statistics and deep neural network. Biomed Signal Process Control 59:101921. ISSN 1746-8094. https://doi.org/10.1016/j.bspc.2020.101921
Anuragi A, Sisodia DS, Pachori RB (2021) Automated FBSE-EWT based learning framework for detection of epileptic seizures using time-segmented EEG signals. Comput Biol Med 136(2021):104708. ISSN 0010-4825. https://doi.org/10.1016/j.compbiomed.2021.104708
Yu Z, Zhou W, Zhang F et al (2019) (2019) Automatic seizure detection based on kernel robust probabilistic collaborative representation. Med Biol Eng Comput 57:205–219. https://doi.org/10.1007/s11517-018-1881-5
Taran S, Bajaj V, Sharma D, Siuly S, Sengur A, (2017) Features based on analytic IMF for classifying motor imagery EEG signals in BCI applications. Measurement 116(2018):68–76. ISSN 0263-2241. https://doi.org/10.1016/j.measurement.2017.10.067
Bhattacharyya A, Pachori RB, Upadhyay A, Acharya UR (2017) (2017) Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals. Appl Sci 7:385. https://doi.org/10.3390/app7040385
Bhattacharyya A, Pachori RB, Upadhyay A, Acharya UR (2017) Tunable-Q wavelet transform based multivariate sub-band fuzzy entropy with application to focal EEG signal analysis. Entropy 2017(19):99
Panda S, Mishra S, Mohanty MN (2021) Epliptic seizure detection and classification using cumulative sum average filter TT-transform and harmony search algorithm based LLRBFN model – Palarch’s. J Archaeol Egypt/Egyptol 17(9):2021. ISSN 1567-214x
Harikumar R, Ganesh Babu C, Gowri Shankar M (2021) Extreme learning machine (ELM) based performance analysis and epilepsy identification from EEG Signals. IETE J Res. https://doi.org/10.1080/03772063.2021.1987997
Peachap AB, Tchiotsop D (2019) Epileptic seizures detection based on some new Laguerre polynomial wavelets, artificial neural networks and support vector machines. Inform Med Unlocked. ISSN 2352-9148. https://doi.org/10.1016/j.imu.2019.100209
Tuncer T, Dogan S, Naik GR (2021) (2021) Epilepsy attacks recognition based on 1D octal pattern, wavelet transform and EEG signals. Multimed Tools Appl 80:25197–25218. https://doi.org/10.1007/s11042-021-10882-4
Baykara M, Abdulrahman A (2021) Seizure detection based on adaptive feature extraction by applying extreme learning machines. Traitement du Signal 38(2):331–340
Zhou J, Zhang X, Jiang Z (2021) Recognition of imbalanced epileptic EEG signals by a graph-based extreme learning machine. Wirel Commun Mob Comput. https://doi.org/10.1155/2021/5871684
Deivasigamani S, Senthilpari C, Yong WH (2021) (2021) Machine learning method based detection and diagnosis for epilepsy in EEG signal. J Ambient Intell Human Comput 12:4215–4221. https://doi.org/10.1007/s12652-020-01816-3
Mishra S, Gelmecha DJ, Singh RS, Rathee DS, Gopikrishna T (2021) Hybrid WCA–SCA and modified FRFCM technique for enhancement and segmentation of brain tumor from magnetic resonance images. Biomed Eng Appl Basis Commun 33(3):2150017. https://doi.org/10.4015/S1016237221500174
Mishra S, Sahu P, Senapati MR (2019) MASCA- PSO based LLRBFNN Model and Improved fast and robust FCM algorithm for detection and classification of brain tumor from MR Image. Evolut Intell. ISSN 1864-5909. https://doi.org/10.1007/s12065-019-00266-x
Xu Y, Fan P, Yuan L (2013) A simple and efficient artificial bee colony algorithm. Math Probl Eng 2013:1–9. https://doi.org/10.1155/2013/526315
Mishra S, Nayak PK, Dash PK, Bisoi R (2016) Comparison of modified TLBO based optimization and extreme learning machine for classification of multiple power signal disturbances. Neural Comput Appl 27(7):2107–2122
Pandey BK, Pandey D, Wariya S (2021) Deep learning and particle swarm optimisation-based techniques for visually impaired humans’ text recognition and identification. Augment Hum Res 6:14. https://doi.org/10.1007/s41133-021-00051-5
Samanta IS, Rout PK, Mishra S (2020) An optimal extreme learning-based classification method for power quality events using fractional Fourier transform. Neural Comput Appli. https://doi.org/10.1007/s00521-020-05282-y
Samanta IS, Mishra S, Rout PK (2019) Power quality events recognition using S-Transform and wild goat optimization based extreme learning machine. Arab J Sci Eng. https://doi.org/10.1007/s13369-019-04289-5
Stefenon SF, Grebogi RB, Freire RZ, Nied A, Meyer LH (2020) Optimized ensemble extreme learning machine for classification of electrical insulators conditions. IEEE Trans Industr Electron 67(6):5170–5178. https://doi.org/10.1109/TIE.2019.2926044
Li B, Li Y, Rong X (2013) The extreme learning machine learning algorithm with tunable activation function. Neural Comput Appl 22(3–4):531–539. https://doi.org/10.1007/s00521-012-0858-9
Bonn University EEG Database. http://epileptologie-bonn.de/cms/frontcontent.php?idcat=193&lang=3. Online (Accessed: 15.7.2018).
Sharma M, Pachori RB, Acharya UR (2017) A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension. Pattern Recogn Lett 94(2017):172–179
Pintas JT, Fernandes LAF, Garcia ACB (2021) Feature selection methods for text classification: a systematic literature review. Artif Intell Rev 54:6149–6200. https://doi.org/10.1007/s10462-021-09970-6
Wang Y, Wang J, Liao H, Chen H (2017) An efficient semi-supervised representatives feature selection algorithm based on information theory. Pattern Recogn 61(2017):511–523. https://doi.org/10.1016/j.patcog.2016.08.011
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Panda, S., Mishra, S. & Mohanty, M.N. Hybrid WCA–PSO Optimized Ensemble Extreme Learning Machine and Wavelet Transform for Detection and Classification of Epileptic Seizure from EEG Signals. Augment Hum Res 8, 4 (2023). https://doi.org/10.1007/s41133-023-00059-z
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DOI: https://doi.org/10.1007/s41133-023-00059-z