Forecasting Epileptic Seizures Using XGBoost Methodology and EEG Signals

Authors

DOI:

https://doi.org/10.4108/eetpht.10.5569

Keywords:

Machine Learning, Epileptic Seizures XGBoost, Electroencephalogram, SMOTE

Abstract

INTRODUCTION: Epilepsy denotes a disorder of neurological origin marked by repetitive and spontaneous seizures without any apparent trigger. Seizures occur due to abrupt and heightened electricity flowing through the brain, which can lead to physical and mental symptoms. There are several types of epileptic seizures, and epilepsy itself can be caused by various underlying conditions. EEG (Electroencephalogram) is one of the most important and widely used tools for epileptic seizure prediction and diagnosis. EEG uses skull sensors to record electrical signals from the brain., and it can provide valuable insights into brain activity patterns associated with seizures.

OBJECTIVES: Brain-computer interface technology pathway for analyzing the EEG signals for seizure prediction to eliminate the class imbalance issue from our dataset in this case, a SMOTE approach is applied.  It is observable that there are more classes of one variable than there are of the others in the output variable. This will be problematic when employing different Artificial intelligence techniques since these algorithms are more likely to be biased towards a certain variable because of its high prevalence

METHODS: SMOTE approaches will be used to address this bias and balance the number of variables in the response variable. To develop an XGBoost (Extreme Gradient Boosting) model using SMOTE techniques to increase classification accuracy.

RESULTS: The results show that the XGBoost method achieves a 98.7% accuracy rate.

CONCLUSION: EEG-based model for seizure type using the XGBoost model for predicting the disease early. The Suggested method could significantly reduce the amount of time needed to accomplish seizure prediction.

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References

Hafeez A. Agboola; Colette Solebo; David S. Aribike; Afolabi E. Lesi; Alfred A. Susu; "Seizure Prediction with Adaptive Feature Representation Learning", JOURNAL OF NEUROLOGY AND NEUROSCIENCE, 2019. DOI: https://doi.org/10.36648/2171-6625.10.2.294

Paolo Detti; Giampaolo Vatti; Garazi Zabalo Manrique de Lara; "EEG Synchronization Analysis for Seizure Prediction: A Study on Data of Noninvasive Recordings", 2020. DOI: https://doi.org/10.3390/pr8070846

Khansa Rasheed; Junaid Qadir; Terence J. O'Brien; Levin Kuhlmann; Adeel Razi; "A Generative Model To Synthesize EEG Data For Epileptic Seizure Prediction", ARXIV-CS.LG, 2020. DOI: https://doi.org/10.1109/TNSRE.2021.3125023

Ercan Coşgun; Anıl Çelebi; "FPGA Based Real-time Epileptic Seizure Prediction System", BIOCYBERNETICS AND BIOMEDICAL ENGINEERING, 2021. DOI: https://doi.org/10.1016/j.bbe.2021.01.006

Ahmed I Sharaf; Mohamed Abu El-Soud; Ibrahim M El-Henawy; "An Automated Approach For Epilepsy Detection Based On Tunable Q -Wavelet And Firefly Feature Selection Algorithm", INTERNATIONAL JOURNAL OF BIOMEDICAL IMAGING, 2018. DOI: https://doi.org/10.1155/2018/5812872

Naghmeh Mahmoodian; Axel Boese; Michael Friebe; Javad Haddadnia; "Epileptic Seizure Detection Using Cross-bispectrum Of Electroencephalogram Signal", SEIZURE, 2019. DOI: https://doi.org/10.1016/j.seizure.2019.02.001

Hisham Daoud; Magdy A Bayoumi; "Efficient Epileptic Seizure Prediction Based On Deep Learning", IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2019. DOI: https://doi.org/10.1109/WF-IoT48130.2020.9221169

Saly Abd-Elateif El-Gindy; Asmaa Hamad; Walid El Shafai; Ashraf A. M. Khalaf; Sami M. El-Dolil; Taha E. Taha; Adel S. El-Fishawy; Turky N. Alotaiby; Saleh A. Alshebeili; Fathi E. Abd El-Samie; "Efficient Communication and EEG Signal Classification in Wavelet Domain for Epilepsy Patients", J. AMBIENT INTELL. HUMANIZ. COMPUT., 2021 DOI: https://doi.org/10.1007/s12652-020-02624-5

P. Suguna; B. Kirubagari; R. Umamaheswari; "An Analysis of Epileptic Seizure Detection and Classification Using Machine Learning-Based Artificial Neural Network", 2021 DOI: https://doi.org/10.1007/978-981-16-1395-1_5

S Kannan; G Premalatha; M Jamuna Rani; D Jayakumar; P Senthil; S Palanivelrajan; S Devi; Kibebe Sahile; "Effective Evaluation of Medical Images Using Artificial Intelligence Techniques", COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022. DOI: https://doi.org/10.1155/2022/8419308

Zhengdao Li; Kai Hwang; Keqin Li; Jie Wu; Tongkai Ji; "Graph-generative Neural Network for EEG-based Epileptic Seizure Detection Via Discovery of Dynamic Brain Functional Connectivity", SCIENTIFIC REPORTS, 2022.

Haneen Alsuradi; Wanjoo Park; Mohamad Eid; "Assessment of EEG-based Functional Connectivity in Response to Haptic Delay", FRONTIERS IN NEUROSCIENCE, 2022. DOI: https://doi.org/10.3389/fnins.2022.961101

Hao Chen; Taoyun Ji; Xiang Zhan; Xiaoxin Liu; Guojing Yu; Wen Wang; Yuwu Jiang; Xiao-Hua Zhou; "An Explainable Statistical Method for Seizure Prediction Using Brain Functional Connectivity from EEG", COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE, 2022. DOI: https://doi.org/10.1155/2022/2183562

Irmak Sivgin; Hasan A. Bedel; Şaban Öztürk; Tolga Çukur; "A Plug-in Graph Neural Network to Boost Temporal Sensitivity in FMRI Analysis", ARXIV-EESS.SP, 2023.

Khaled Mohamad Almustafa,Classification of epileptic seizure dataset using different machine learning algorithms,

Informatics in Medicine Unlocked, Volume 21,2020,100444, ISSN 2352-9148. DOI: https://doi.org/10.1016/j.imu.2020.100444

H O Lekshmy1, Dhanyalaxmi Panickar1 and Sandhya Harikumar12022 J. Phys. Comparative analysis of multiple machine learning algorithms for epileptic seizure prediction: Conf. Ser. 2161 012055. DOI 10.1088/1742-6596/2161/1/012055. DOI: https://doi.org/10.1088/1742-6596/2161/1/012055

Ghosh, H., Tusher, M.A., Rahat, I.S., Khasim, S., Mohanty, S.N. (2023). Water Quality Assessment Through Predictive Machine Learning. In: Intelligent Computing and Networking. IC-ICN 2023. Lecture Notes in Networks and Systems, vol 699. Springer, Singapore. https://doi.org/10.1007/978-981-99-3177-4_6 DOI: https://doi.org/10.1007/978-981-99-3177-4_6

Rahat IS, Ghosh H, Shaik K, Khasim S, Rajaram G. Unraveling the Heterogeneity of Lower-Grade Gliomas: Deep Learning-Assisted Flair Segmentation and Genomic Analysis of Brain MR Images. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Sep. 29 [cited 2023 Oct. 2];9. https://doi.org/10.4108/eetpht.9.4016 DOI: https://doi.org/10.4108/eetpht.9.4016

Ghosh H, Rahat IS, Shaik K, Khasim S, Yesubabu M. Potato Leaf Disease Recognition and Prediction using Convolutional Neural Networks. EAI Endorsed Scal Inf Syst [Internet]. 2023 Sep. 21 https://doi.org/10.4108/eetsis.3937 DOI: https://doi.org/10.4108/eetsis.3937

Mandava, S. R. Vinta, H. Ghosh, and I. S. Rahat, “An All-Inclusive Machine Learning and Deep Learning Method for Forecasting Cardiovascular Disease in Bangladeshi Population”, EAI Endorsed Trans Perv Health Tech, vol. 9, Oct. 2023. https://doi.org/10.4108/eetpht.9.4052 DOI: https://doi.org/10.4108/eetpht.9.4052

Mandava, M.; Vinta, S. R.; Ghosh, H.; Rahat, I. S. Identification and Categorization of Yellow Rust Infection in Wheat through Deep Learning Techniques. EAI Endorsed Trans IoT 2023, 10. https://doi.org/10.4108/eetiot.4603 DOI: https://doi.org/10.4108/eetiot.4603

Khasim, I. S. Rahat, H. Ghosh, K. Shaik, and S. K. Panda, “Using Deep Learning and Machine Learning: Real-Time Discernment and Diagnostics of Rice-Leaf Diseases in Bangladesh”, EAI Endorsed Trans IoT, vol. 10, Dec. 2023 https://doi.org/10.4108/eetiot.4579 DOI: https://doi.org/10.4108/eetiot.4579

Khasim, H. Ghosh, I. S. Rahat, K. Shaik, and M. Yesubabu, “Deciphering Microorganisms through Intelligent Image Recognition: Machine Learning and Deep Learning Approaches, Challenges, and Advancements”, EAI Endorsed Trans IoT, vol. 10, Nov. 2023. https://doi.org/10.4108/eetiot.4484 DOI: https://doi.org/10.4108/eetiot.4484

Mohanty, S.N.; Ghosh, H.; Rahat, I.S.; Reddy, C.V.R. Advanced Deep Learning Models for Corn Leaf Disease Classification: A Field Study in Bangladesh. Eng. Proc. 2023, 59, 69. https://doi.org/10.3390/engproc2023059069 DOI: https://doi.org/10.3390/engproc2023059069

Alenezi, F.; Armghan, A.; Mohanty, S.N.; Jhaveri, R.H.; Tiwari, P. Block-Greedy and CNN Based Underwater Image Dehazing for Novel Depth Estimation and Optimal Ambient Light. Water 2021, 13, 3470. https://doi.org/10.3390/w13233470 DOI: https://doi.org/10.3390/w13233470

Werner de Vargas, V., Schneider Aranda, J.A., dos Santos Costa, R. et al. Imbalanced data preprocessing techniques for machine learning: a systematic mapping study. Knowl Inf Syst 65, 31–57 (2023). https://doi.org/10.1007/s10115-022-01772-8 DOI: https://doi.org/10.1007/s10115-022-01772-8

L. Wei and C. Mooney, "Epileptic Seizure Detection in Clinical EEGs Using an XGboost-based Method," 2020 IEEE Signal Processing in Medicine and Biology Symposium (SPMB), Philadelphia, PA, USA, 2020, pp. 1-6, doi: 10.1109/SPMB50085.2020.9353625. DOI: https://doi.org/10.1109/SPMB50085.2020.9353625

Shafiezadeh S, Duma GM, Mento G, Danieli A, Antoniazzi L, Del Popolo Cristaldi F, Bonanni P, Testolin A. Methodological Issues in Evaluating Machine Learning Models for EEG Seizure Prediction: Good Cross-Validation Accuracy Does Not Guarantee Generalization to New Patients. Applied Sciences. 2023; 13(7):4262. https://doi.org/10.3390/app13074262 DOI: https://doi.org/10.3390/app13074262

Elika Karbassiyazdi, Fatemeh Fattahi, Negin Yousefi, Amirhessam Tahmassebi, Arsia Afshar Taromi, Javad Zyaie Manzari, Amir H Gandomi, Ali Altaee, Amir Razmjou, XGBoost model as an efficient machine learning approach for PFAS removal: Effects of material characteristics and operation conditions, Environmental Research, Volume 215, Part 1,2022,114286, ISSN 0013-9351, https://doi.org/10.1016/j.envres. DOI: https://doi.org/10.1016/j.envres.2022.114286

Hägglund, M. "Optimization of Pre-Ictal Interval Time Period for Epileptic Seizure Prediction." caring is sharing–exploiting the value in data for health and innovation (2023): 232.

K. Manasvi Bhat, P. P. Anchalia, S. Yashashree, R. Sanjeetha and A. Kanavalli, "Detection and Prediction of the Preictal State of an Epileptic Seizure using Machine Learning Techniques on EEG Data," 2019 IEEE Bombay Section Signature Conference (IBSSC), Mumbai, India, 2019, pp. 1-5, doi: 10.1109/IBSSC47189.2019.8972992. DOI: https://doi.org/10.1109/IBSSC47189.2019.8972992

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Published

27-03-2024

How to Cite

1.
Mounika S, S R R. Forecasting Epileptic Seizures Using XGBoost Methodology and EEG Signals. EAI Endorsed Trans Perv Health Tech [Internet]. 2024 Mar. 27 [cited 2024 Apr. 25];10. Available from: https://publications.eai.eu/index.php/phat/article/view/5569