Gradient Boosting for Predicting the Relation Between Bio-medical Signals and Seizures Using LGBM and XGBoost
DOI:
https://doi.org/10.13052/jmm1550-4646.2025Keywords:
Electroencephalogram, ensemble learnings, seizure prediction, XGBoost, LGBM, attribute rankingAbstract
Background and aim: In recent years, research in the fields of brain-computer interfacing techniques and related areas are developing at a very rapid rate with the help of exploding of Artificial Intelligence, Machine Learning and Deep Learning. A new concept of Gradient Boosting has become popular research area among the researchers related to the field of automatic classification of Electroencephalograph (EEG) signals for predication of mental health issues like seizures.
Methods: However effective feature extraction from EEG and accurately classify them with efficient classifiers is still an important task and attracted wide attention in this area. Therefore in this paper, we presented the detailed mathematical analysis of these methods and ensemble learnings based EEG signals classification method for seizures classification in EEG using Extreme Gradient Boosting Model such as Light Gradient Boosting Machine Learning (LGBM) and XGBoost.
Results: Time-frequency domain based non-linear features are selected from preprocessed EEG Dataset, and PCA (Principal Component Analysis) is used for dimensionality reduction for features engineering, then optimized feature based training and testing is done for two class classification in ensemble learning method i.e. LGBM and XGBoost. Finally, both models are tested with dataset of University of Bonn, Germany to classify the signals.
Conclusions: In addition this paper highlights the Correlation Analysis Methodology to Identify Strong Predictor and Attributes Correlation-based Attribute Ranking for the Feature Engineering which has proved to be more efficient in EEG signals Classification and provide comparative analysis with other existing models for performance evaluation in terms of accuracy which is 87.34 and 92.31 for LBGM and XGBoost, sensitivity of 85.21 and 90.18 and specificity of 83.0 and 90.04 for LBGM and XGBoost.
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Bairagi, V. EEG signal analysis for early diagnosis of Alzheimer’s disease using spectral and wavelet based features. Int. j. inf. tecnol. 10, 403–412 (2018). https://doi.org/10.1007/s41870-018-0165-5 (Journal).
Chakraborti, S., Choudhary, A., Singh, A. et al. A machine learning-based method to detect epilepsy. Int. j. inf. tecnol. 10, 257–263 (2018). https://doi.org/10.1007/s41870-018-0088-1 (Journal).
Berg, A.T., Berkovic, S.F., Brodie, M.J., Buchhalter, J., Cross, J.H., Van Emde Boas, W., Engel, J., French, J., Glauser, T.A., Mathern, G.W., Moshé, S.L., Nordli, D., Plouin, P. and Scheffer, I.E. (2010), Revised terminology and concepts for organization of seizures and epilepsies: Report of the ILAE Commission on Classification and Terminology, 2005–2009. Epilepsia, 51: 676–685. https://doi.org/10.1111/j.1528-1167.2010.02522.x (Journal).
Dias, N. S., Kamrunnahar, M., Mendes, P. M., Schiff, S. J., and Correia, J. H. (2007). Comparison of EEG pattern classification methods for brain-computer interfaces. Annual International Conference of the IEEE Engineering in Medicine and Biology – Proceedings, 2540–2543. (Conference).
Gotman J. Automatic seizure detection: improvements and evaluation. Electroencephalogr Clin Neurophysiol. 1990;76(4):317–324. (Journal).
Gupta, M.K., Chandra, P. A comprehensive survey of data mining. Int. j. inf. tecnol. 12, 1243–1257 (2020). https://doi.org/10.1007/s41870-020-00427-7 (Journal).
Le Van Quyen, M., Martinerie, J., Navarro, V., Boon, P., D’Havé, M., Adam, C., Renault, B., Varela, F. and Baulac, M., “Anticipation of epileptic seizures from standard EEG recordings”, The Lancet, vol. 357, no. 9251, pp. 183–188, 2001. (Journal).
Büyükçakır, B., Elmaz, F. and Mutlu, A.Y., “Hilbert vibration decomposition-based epileptic seizure prediction with neural network”, Computers in biology and medicine, vol. 119, pp. 103665, 2020. (Journal).
Hirald Dwaraka Praveena, C. Subhas and K. Rama Naidu, “Automatic epileptic seizure recognition using reliefF feature selection and long short term memory classifier”, Journal of Ambient Intelligence and Humanized Computing, 2020. (Journal).
Athar A. Ein Shoka, Monagi H. Alkinani, A. S. El-Sherbeny, Ayman El-Sayed and Mohamed M. Dessouky, “Automated seizure diagnosis system based on feature extraction and channel selection using EEG signals,” Brain Informatics, vol. 8, no. 1, 2021. (Journal).
Ratnaprabha Ravindra Borhade and Nagmode, M.S., “Modified Atom Search Optimization-based Deep Recurrent Neural Network for epileptic seizure prediction using electroencephalogram signals”, Biocybernetics and Biomedical Engineering, vol. 40, no. 4, pp. 1638–1653, 2020. (Journal).
Prathaban, B.P. and Balasubramanian, R., “Prediction of epileptic seizures using Grey Wolf Optimized Model Driven mathematical approach”, Microprocessors and Microsystems, pp. 103370, 2020. (Journal).
Mustafa Sameer and Bharat Gupta, “Detection of epileptical seizures based on alpha band statistical features”, Wireless Personal Communications, vol. 115, pp. 909–925, 2020.
Thara, D.K., PremaSudha, B.G. and Xiong, F., “Epileptic seizure detection and prediction using stacked bidirectional long short term memory”, Pattern Recognition Letters, vol. 128, pp. 529–535, 2019.
Marzieh Savadkoohi, Oladunni, T. and Thompson, L., “A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal”, Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 1328–1341, 2020.
Afshin Shoeibi, Navid Ghassemi, Roohallah Alizadehsani, Modjtaba Rouhani, Hossein Hosseini-Nejad, Abbas Khosravi, Maryam Panahiazar, Saeid Nahavandi, A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals, Expert Systems with Applications, Volume 163, 2021, 113788, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2020.113788 (Journal).
Jukic S, Saracevic M, Subasi A, Kevric J. Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals. Mathematics. 2020; 8(9):1481. https://doi.org/10.3390/math8091481 (Journal).
Rekha Sahu, Satya Ranjan Dash, Lleuvelyn A Cacha, Roman R Poznanski, Shantipriya Parida. Epileptic seizure detection: a comparative study between deep and traditional machine learning techniques. Journal of Integrative Neuroscience, 2020, 19(1): 1–9. (Journal).
Saleh A. Alshebeili, Ahmed Sedik, Basma Abd El-Rahiem, Turky N. Alotaiby, Ghada M. El Banby, Heba A. El-Khobby, Mahmoud A.A. Ali, Ashraf A.M. Khalaf, Fathi E. Abd El-Samie, Inspection of EEG signals for efficient seizure prediction, Applied Acoustics, Volume 166, 2020, 107327, ISSN 0003-682X, https://doi.org/10.1016/j.apacoust.2020.107327 (Journal).
Siddiqui, M. K., Morales-Menendez, R., Huang, X., and Hussain, N. (2020). A review of epileptic seizure detection using machine learning classifiers. Brain informatics, 7, 1–18. (Journal).
Hosseini, M. P., Hajisami, A., and Pompili, D. (2016, July). Real-time epileptic seizure detection from eeg signals via random subspace ensemble learning. In 2016 IEEE international conference on autonomic computing (ICAC) (pp. 209–218). IEEE. (Conference).
Hosseini, M. P., Pompili, D., Elisevich, K., and Soltanian-Zadeh, H. (2018). Random ensemble learning for EEG classification. Artificial intelligence in medicine, 84, 146–158. (Journal).
Hosseini, M. P., Hosseini, A., and Ahi, K. (2020). A Review on Machine Learning for EEG Signal Processing in Bioengineering. IEEE reviews in biomedical engineering. (Journal)
Ren, W., Han, M. Classification of EEG Signals Using Hybrid Feature Extraction and Ensemble Extreme Learning Machine. Neural Process Lett 50, 1281–1301 (2019). https://doi.org/10.1007/s11063-018-9919-0 (Journal).
Abdulhay, E., Elamaran, V., Chandrasekar, M., Balaji, V. S., and Narasimhan, K. (2017). Automated diagnosis of epilepsy from EEG signals using ensemble learning approach. Pattern Recognition Letters. (Journal).
Usman, S. M., Usman, M., and Fong, S. (2017). Epileptic seizures prediction using machine learning methods. Computational and mathematical methods in medicine, 2017. (Journal).
Bhattacharyya, S., Konar, A., Tibarewala, D. N., Khasnobish, A., and Janarthanan, R. (2014, January). Performance analysis of ensemble methods for multi-class classification of motor imagery EEG signal. In Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) (pp. 712–716). IEEE. (Conference).
Khakon Das, Debashis Daschakladar, Partha Pratim Roy, Atri Chatterjee, Shankar Prasad Saha, “Epileptic seizure prediction by the detection of seizure waveform from the pre-ictal phase of EEG signal,” Biomedical Signal Processing and Control, Vol. 57, 2020. (Journal).
Marzieh Savadkoohi, Timothy Oladunni, and Lara Thompson, “A machine learning approach to epileptic seizure prediction using Electroencephalogram (EEG) Signal”, Biocybernetics and Biomedical Engineering, vol. 40, no. 3, pp. 1328–1341, 2020. (Journal).
Itaf Ben Slimen, Larbi Boubchir, and Hassene Seddik, “Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states”, The journal of biomedical research, vol. 34, no. 3, 2020. (Journal).
Syed Muhammad Usman, Shehzad Khalid, and Zafar Bashir, “Epileptic seizure prediction using scalp electroencephalogram signals”, Biocybernetics and biomedical engineering, vol. 41, pp. 1–10, 2021 (Journal).
Satapathy, S. K., Dehuri, S., and Jagadev, A. K. (2016). An empirical analysis of different machine learning techniques for classification of EEG signal to detect epileptic seizure. International Journal of Applied Engineering Research, 11(1), 120–129. (Journal).
Imah, E. M., and Widodo, A. (2017, October). A comparative study of machine learning algorithms for epileptic seizure classification on eeg signals. In 2017 International Conference on Advanced Computer Science and Information Systems (ICACSIS) (pp. 401–408). IEEE. (Conference).
Usman, S. M., Usman, M., and Fong, S. (2017). Epileptic seizures prediction using machine learning methods. Computational and mathematical methods in medicine, 2017. (Journal).
Sun, S., Zhang, C., and Zhang, D. (2007). An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters, 28(15), 2157–2163. (Journal).
Abbasi, B., and Goldenholz, D. M. (2019). Machine learning applications in epilepsy. Epilepsia, 60(10), 2037–2047. (Journal).
Abualsaud, K., Mahmuddin, M., Saleh, M., and Mohamed, A. (2015). Ensemble classifier for epileptic seizure detection for imperfect EEG data. The Scientific World Journal, 2015. (Journal).
Bhattacharyya, S., Konar, A., Tibarewala, D. N., Khasnobish, A., and Janarthanan, R. (2014, January). Performance analysis of ensemble methods for multi-class classification of motor imagery EEG signal. In Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) (pp. 712–716). IEEE. (Journal).
Sun, S., Zhang, C., and Zhang, D. (2007). An experimental evaluation of ensemble methods for EEG signal classification. Pattern Recognition Letters, 28(15), 2157–2163. (Journal).
Jukic S, Saracevic M, Subasi A, Kevric J. Comparison of Ensemble Machine Learning Methods for Automated Classification of Focal and Non-Focal Epileptic EEG Signals. Mathematics. 2020; 8(9):1481. https://doi.org/10.3390/math8091481 (Journal).
Wang, S., Li, Y., Wen, P., and Lai, D. (2016). Data selection in EEG signals classification. Australasian physical & engineering sciences in medicine, 39(1), 157–165. (Journal).
Andrzejak, R. G., Lehnertz, K., Mormann, F., Rieke, C., David, P., and Elger, C. E. (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. (Journal).
University of bonn Database, https://epilepsy.uni-freiburg.de/database (Accessed on June 2022).
Putatunda, S., and Rama, K. (2018, November). A comparative analysis of hyperopt as against other approaches for hyper-parameter optimization of XGBoost. In Proceedings of the 2018 International Conference on Signal Processing and Machine Learning (pp. 6–10). (Conference).
S.-E. Ryu, D.-H. Shin and K. Chung, “Prediction Model of Dementia Risk Based on XGBoost Using Derived Variable Extraction and Hyper Parameter Optimization,” in IEEE Access, vol. 8, pp. 177708–177720, 2020, doi: https://doi.org/10.1109/ACCESS.2020.3025553 (Journal).
Sommer, J., Sarigiannis, D., and Parnell, T. (2019). Learning to Tune XGBoost with XGBoost. arXiv preprint arXiv:1909.07218. (Journal).
Wang, Y., and Ni, X. (2019). A XGBoost risk model via feature selection and Bayesian hyper-parameter optimization. ArXiv, abs/1901.08433. (Journal).
Han, J., Pei, J., and Kamber, M. (2011). Data mining: concepts and techniques. Elsevier. (Journal).
Kumar, S., and Chong, I. (2018). Correlation Analysis to Identify the Effective Data in Machine Learning: Prediction of Depressive Disorder and Emotion States. International journal of environmental research and public health, 15(12), 2907. https://doi.org/10.3390/ijerph15122907. (Journal).
Hall, M.A. (2003). Correlation-based Feature Selection for Machine Learning (Web source).
