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Xavier-PSO-ELM-based EEG signal classification method for predicting epileptic seizures

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Abstract

Epilepsy represents one of the most common neurological diseases that affects a substantial number of individuals worldwide, which is characterized by recurrent, unprovoked seizures detectable via electroencephalogram (EEG). To address this issue, we propose an Extreme Learning Machine (ELM) model for seizure prediction. Firstly, we optimized the ELM with a single hidden-layer feed-forward network using Particle Swarm Optimization (PSO) as a meta-heuristic. Secondly, we employed the Xavier method to initialize random variables and improve model performance. Our optimized classification model was evaluated using a dataset from the University of Bonn. Results from the experiments demonstrate our model's excellent classification performance with 98.13% sensitivity and 91.04% specificity.

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Data availability

The dataset analyzed during the current study have been available in UCI Machine Learning Repository: https: //archive.ics.uci.edu/ml/ datasets/ Epileptic + Seizure + Recognition. The pre-processed version is available in the following Kaggle datasets repository: Epileptic Seizure Recognition | Kaggle.

Notes

  1. Forscher, “Epileptologie bonn / forschung / ag lehnertz / eeg data download,” www.meb.uni-bonn.de. [Online]. Available: http://www.meb.uni-bonn.de/epileptologie/science/physik/eegdata.html.

  2. Forscher, “Epileptologie bonn / forschung / ag lehnertz / eeg data download,” www.meb.uni-bonn.de. [Online]. Available: http://www.meb.unibonn.de/epileptologie/science/physik/eegdata.html.

References

  1. https://archive.ics.uci.edu/ml/datasets/Epileptic+Seizure+Recognition (accessed on 20 February 2022)

  2. Acharya UR, Oh SL, Hagiwara Y, Tan JH, Adeli H (2018) Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 100:270–278

    Article  PubMed  Google Scholar 

  3. Akyol K (2020) Stacking ensemble based deep neural networks modeling for effective epileptic seizure detection. Expert Syst Appl 148(C):113239

    Article  Google Scholar 

  4. Almustafa KM (2020) Classification of epileptic seizure dataset using different machine. Inf Med 21(2020):100444

    Google Scholar 

  5. Alotaiby T, Alshebeili S, Alotaibi F, Alrshoud S (2017) Epileptic seizure prediction using csp and lda for scalp eeg signals, Comput. Intell Neurosci, Article ID 1240323:1–11

    Google Scholar 

  6. Alshebeili SA, Sedik A, Abd E-R, Alotaiby TN, El Banby GM, El-Khobby HA, Ali MAA, Khalaf AA, Abd El-Samie FE (2020) Inspection of EEG signals for efficient seizure prediction. Appl Acoust 166(2020):107327

    Article  Google Scholar 

  7. Andrzejak RG, Lehnertz K, Mormann F, Rieke C, David P, Elger CE (2001) Indications of nonlinear deterministic and finite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E 64(6):061907. https://doi.org/10.1103/PhysRevE.64.061907

    Article  ADS  CAS  Google Scholar 

  8. Bangyal H, Ahmad J, Rauf HT, Pervaiz S (2018) An overview of mutation strategies in bat algorithm. Int J Adv Comput Sci Appl (IJACSA) 9(8):523–534

    Google Scholar 

  9. Bangyal WH, Ahmed J, Rauf HT (2020) A modified bat algorithm with torus walk for solving global optimisation problems. Int J Bio-Ins Comput 15:1–13

    Article  Google Scholar 

  10. Bangyal WH, Hameed A, Alosaimi W, Alyami H (2021) A new initialization approach in particle swarm optimization for global optimization problems. Comput Intell Neurosci, 2021, Article ID 6628889, 17, 2021, https://doi.org/10.1155/2021/6628889

  11. Behnam M, Pourghassem H (2016) Real-time seizure prediction using rls filtering and interpolated histogram feature based on hybrid optimization algorithm of bayesian classifier and hunting search. Comput Methods Programs Biomed 132:115–136

    Article  PubMed  Google Scholar 

  12. Berrar D (2018) Cross-validation. Encycloped Bioinformat Comput Biol

  13. Bizopoulos P, Lambrou GI, Koutsouris D (2019) Signal2image modules in deep neural networks for EEG classification, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Berlin, Germany, July 23–27, pp. 702–705

  14. Brownlee J (2020) Transfer learning in keras with computer vision models. Machine learning mastery: 2020. Available online: https: //machinelearningmastery.com/how-to-use-transfer-learning-when-developing-convolutional-neural-network-models/ (accessed on 25 October 2020).

  15. Chandra JG, Anshuman S, Anupam A (2019) Performance analysis of supervised machine learning algorithms for epileptic seizure detection with high variability EEG datasets: A comparative study, International Conference on Electrical, Electronics and Computer Engineering (UPCON), pp. 1–6

  16. Cho D, Min B, Kim J, Lee B (2016) EEG-based prediction of epileptic seizures using phase synchronization elicited from noise-assisted multivariate empirical mode decomposition. IEEE Trans Neu Syst Rehab Eng 25(8):1309–1318

    Article  Google Scholar 

  17. Chu H, Chung CK, Jeong W, Cho KH (2017) Predicting epileptic seizures from scalp EEG based on attractor state analysis. Comput Methods Prog Biomed, pp. 75–87

  18. Ding S, Xu X, Nie R (2014) Extreme learning machine and its applications. Neural Comput Appl 25:549–556

    Article  ADS  Google Scholar 

  19. Falco-Waltera JJ, Scheffer IE, Fisher RS (2018) The new definition and classification of seizures and epilepsy. Epilepsy Res 139:73–79

    Article  Google Scholar 

  20. Fei K, Wang W, Yang Q, Tang S (2017) Chaos feature study in fractional fourier domain for preictal prediction of epileptic seizure. Neurocomputing 249(2):290–298

    Article  Google Scholar 

  21. Glorot X, Bengio Y (2010) Understanding the difficulty of training deep feedforward neural networks. Proc Thirteenth Int Conf Artif Intell Stat PMLR 9:249–256

    Google Scholar 

  22. Huang GB, Zhu QY, Siew CK (2004) Extreme learning machine: a new learning scheme of feedforward neural networks. IEEE Int Joint Conf Neur Netw 2:985–990

    Google Scholar 

  23. Jana GC, Sabath A, Agrawal A (2019) Performance analysis of supervised machine learning algorithms for epileptic seizure detection with high variability EEG datasets: A Comparative Study. 2019 International Conference on Electrical, Electronics and Computer Engineering (UPCON), ALIGARH, India, pp. 1–6

  24. Karal O (2020) Performance comparison of different kernel functions in SVM for different k value in k-fold cross-validation. InnovIntell Syst Appl Conf. pp. 1–5

  25. Kennedy J, Eberhart R (1995), Particle swarm optimization. Proceedings of ICNN'95.Int Conf Neur Netw. 4, 1942–1948. https://doi.org/10.1109/ICNN.1995.488968

  26. Khan H, Marcuse L, Fields M, Swann K, Yener B (2017) Focal onset seizure prediction using convolutional networks. IEEE Trans Biomed Eng 65(9):2109–2118

    Article  PubMed  Google Scholar 

  27. Kirichenko L, Radivilova T, Bulakh V, Zinchenko P, Alghawli AS (2020) Two approaches to machine learning classification of time series based on recurrence plots. In: IEEE Third International Conference on Data Stream Mining and Processing, Lviv, Ukraine, pp. 84–89

  28. Leilei S, Bo J, Haoyu Y, Jianing T, Chuanren L, Hui X (2019) Unsupervised EEG feature extraction based on echo state network. Inf Sci 475:1–17

    Article  Google Scholar 

  29. Liu C-L, Xiao B, Hsaio W-H, Tseng VS (2019) Epileptic seizure prediction with multi-view convolutional neural networks. IEEE Access 7:170352–170361

    Article  Google Scholar 

  30. Mahmood SF, Marhaban MH, Rokhani FZ, Samsudin K, Arigbabu OA (2017) Fasta-elm: a fast adaptive shrinkage/thresholding algorithm for extreme learning machine and its application to gender recognition. Neurocomputing 219:312–322

    Article  Google Scholar 

  31. Mamli S, Kalbkhani H (2019) Gray-level co-occurrence matrix of fourier synchro-squeezed transform for epileptic seizure detection. Biocybernet Biomed Eng 39(1):87–99

    Article  Google Scholar 

  32. Marini F, Walczak B (2015) Particle swarm optimization (PSO). A tutorial. Chemom Intell Lab Syst 149:153–165

    Article  CAS  Google Scholar 

  33. Myers M, Padmanabha A, Hossain G, de Jongh Curry A, Blaha C (2016) Seizure prediction and detection via phase and amplitude lock values. Front Hum Neurosci 10(80):1–9

    Google Scholar 

  34. Nkengfack LC, Tchiotsop D, Atangana R, Door V, Wolf D (2021) Classification of EEG signals for epileptic seizures detection and eye states identification using Jacobi polynomial transforms-based measures of complexity and least-square support vector machine. Inf Med Unlocked 23(9):1–13

    Google Scholar 

  35. Oprea S-V, Bâra A (2021) Machine learning classification algorithms and anomaly detection in conventional meters and Tunisian electricity consumption large datasets. Comput Electric Eng. 94:107329

    Article  Google Scholar 

  36. Powers D (2011) Evaluation: From Precision, Recall and F-Measure to ROC, Informedness, Markedness & Correlation. J Mach Learn Technol 2(1):37–63

    MathSciNet  Google Scholar 

  37. Prathaban BP, Balasubramanian R (2021) Dynamic learning framework for epileptic seizure prediction using sparsity-based EEG reconstruction with optimized CNN classifier. Expert Syst Appl 170:2411–2502

    Google Scholar 

  38. Rahman R, Varnosfaderani SM, Makke O, Sarhan NJ, Asano E, Luat A, Alhawari M (2021) Comprehensive Analysis of EEG Datasets for Epileptic Seizure Prediction. IEEE International Symposium on Circuits and Systems (ISCAS), Daegu, Korea, pp. 1–5

  39. Ramchoun H, Idrissi MA, Ghanou Y, Ettaouil M (2017) Multilayer perceptron: architecture optimization and training. Int J Int Multimed Artif Intell 4(1):26–30

    Google Scholar 

  40. Ramyachitra D, Manikandan P (2014) Imbalanced dataset classification and solutions: A review, International Journal of Computing and Business Research (IJCBR), 5(4)

  41. Ranjith E, Parthiban L (2019) Evaluation of neural networks and feed forward neural network models on to content-based image retrieval. International Conference on Inventive Systems and Control, Coimbatore, India

  42. Savadkoohi M, Oladduni T (2020) A machine learning approach to epileptic seizure prediction using electroencephalogram (EEG) signal. Biocybern Biomed Eng 40(3):1328–1341

    Article  PubMed  PubMed Central  Google Scholar 

  43. Shao Z, Joo EM, Ning W (2016) An efficient leave-one-out cross-validation-based extreme learning machine (ELOO-ELM) with minimal user intervention. IEEE Trans Cybern 46(8):1939–1951

    Article  PubMed  Google Scholar 

  44. Siuly S, Zhang Y (2016) Medical big data: neurological diseases diagnosis through medical data analysis. Data Sci Eng 1:54–64

    Article  Google Scholar 

  45. Slimen IB, Boubchir L, Mbarki Z, Seddik H (2020) EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms. J Biomed Res 34(3):151–161

    Article  PubMed  PubMed Central  Google Scholar 

  46. Subasi A, Kevric J, Canbaz MA (2019) Epileptic seizure detection using hybrid machine learning methods. Neural Comput Appl 31(1):317–325

    Article  Google Scholar 

  47. Sun L, Jin B, Yang H, Tong J, Liu C, Xiong H (2018) Unsupervised EEG feature extraction based on echo state network. Inf Sci 475:1–17

    Article  ADS  Google Scholar 

  48. Tan P, Sa W, Yu, L (2016) Applying extreme learning machine to classification of EEG BCI. In 2016 IEEE International Conference on Cyber Technology in Automation, Control, and Intelligent Systems, pp. 228–232

  49. Truong ND, Nguyen AD, Kuhlmann L, Bonyadi MR, Yang J, Ippolito S, Kavehei O (2018) Convolutional neural networks for seizure prediction using intracranial and scalp electroencephalogram. Neural Netw 105:104–111

    Article  PubMed  Google Scholar 

  50. Tsiouris K, Pezoulas V, Zervakis M, Konitsiotis S, Koutsouris D, Fotiadis DI (2018) A long short-term memory deep learning network for the prediction of epileptic seizures using EEG signals. Comput Biol Med 99:24–37

    Article  PubMed  Google Scholar 

  51. Usman SM, Khalid S, Aslam MH (2020) Epileptic seizures prediction using deep learning techniques, IEEE Access, pp. 39–99, 840 007

  52. Vuttipittayamongkol P, Eyadn E (2020) Improved overlap-based undersampling for imbalanced dataset classication with application to epilepsy. Int J Neural Syst, 30(8):2050043. Available from: https://doi.org/10.1142/S0129065720500434

  53. Wei X, Zhou L, Zhang Z, Chen Z, Zhou Y (2019) Early prediction of epileptic seizures using a long-term recurrent convolutional network. J Neurosci Methods 327:108395

    Article  PubMed  Google Scholar 

  54. Ya-Guan Q, Jun M, Xi-Min Z, Jun P, Wu-Jie Z, Shu-Hui W, Ben-Sheng Y, Jing-Sheng L (2020) EMSGD: An improved learning algorithm of neural networks with imbalanced data, IEEE Access, 8, pp. 64086–64098

  55. Zemzam M, Elhami N, Itmi M, Hmina N (2019) A modified particle swarm optimization algorithm linking dynamic neighborhood topology to parallel computation. Int J Eng Technol (IJATCSE) 8(2):112–118

    Google Scholar 

  56. Zhang S, Zhen F, Wang B, Li Z, Qin X (2020) Coupling social media and agent-based modelling: A novel approach for supporting smart tourism planning. J Urban Technol, pp. 119

  57. Zubair M, Belykh MV, Naik MUK, Gouher MFM, Vishwakarma S, Ahamed SR, Kongara R (2021) Detection of epileptic seizures from EEG signals by combining dimensionality reduction algorithms with machine learning models. IEEE Sensors Journal, TechRxiv. Preprint. https://doi.org/10.36227/techrxiv.14378888.v1

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Funding

The research leading to these results has received funding from the Ministry of Higher Education and Scientific Research of Tunisia under the grant agreement number LR11ES48.

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Authors and Affiliations

  1. Research Groups in Intelligent Machines Lab, BP 3038, Sfax, Tunisia

    Aymen Laifi, Emna Benmohamed & Hela Ltifi

  2. Department of Cyber Security, College of Engineering and Information Technology, Onaizah Colleges, P.O. Box 5371, Onaizah, Kingdom of Saudi Arabia

    Emna Benmohamed

  3. Faculty of Sciences and Technology of Sidi Bouzid, University of Kairouan, Kairouan, Tunisia

    Hela Ltifi

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  1. Aymen Laifi
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Correspondence to Emna Benmohamed.

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Laifi, A., Benmohamed, E. & Ltifi, H. Xavier-PSO-ELM-based EEG signal classification method for predicting epileptic seizures. Multimed Tools Appl 83, 30675–30696 (2024). https://doi.org/10.1007/s11042-023-16514-3

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