International Journal of Engineering and Manufacturing (IJEM)
IJEM Vol. 16, No. 5, 8 Oct. 2026
Cover page and Table of Contents: PDF (size: 1446KB)
Leakage-Controlled Recording-Level Evaluation of Machine Learning, Deep Learning, and Convolutional-Kernel Models for EEG-Based Epileptic Seizure Detection
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Author(s)
Index Terms
EEG seizure detection, recording-grouped cross-validation, leakage-controlled evaluation, machine learning, deep learning, convolutional-kernel time-series classification, computational efficiency
Abstract
Electroencephalogram (EEG)-based seizure detection is a challenging time-series classification problem because EEG signals are nonlinear, non-stationary, and temporally heterogeneous. This study presents a unified comparison of classical machine-learning, neural and deep-learning, convolutional-kernel time-series, and probability-level ensemble models using the Epileptic Seizure Recognition benchmark dataset. The 11,500 EEG segments were reconstructed into 500 recording groups, and recording-grouped nested cross-validation was applied to prevent segments from the same recording from crossing training and test partitions. Depending on the modelling paradigm, segments were represented either by 67 handcrafted time-, frequency-, and nonlinear-domain features or by ordered 178-sample EEG sequences. Recording-level average precision was used as the primary evaluation metric, supported by ROC-AUC, threshold-dependent measures, Brier score, bootstrap confidence intervals, ablation analysis, explainability, and computational-efficiency benchmarking. RBF-SVM achieved the highest average precision of 0.997985 and ROC-AUC of 0.999475. MultiRocket produced the lowest family-representative Brier score of 0.006148, while MLP achieved the lowest standalone inference latency after feature extraction. The selected meta-ensemble produced strong threshold-dependent performance but did not improve overall precision–recall ranking. Paired bootstrap analysis found no statistically significant differences among the family representatives after multiple-comparison correction. These findings demonstrate that handcrafted-feature and time-series models can both achieve strong recording-level performance when evaluated under a consistent, leakage-controlled protocol. Because verified patient identifiers were unavailable, the results should not be interpreted as evidence of patient-independent clinical generalization.
Cite This Paper
Sanagavarapu Sunitha, Umadevi Ramamoorthy, "Leakage-Controlled Recording-Level Evaluation of Machine Learning, Deep Learning, and Convolutional-Kernel Models for EEG-Based Epileptic Seizure Detection", International Journal of Engineering and Manufacturing (IJEM), Vol.16, No.5, pp. 179-204, 2026. DOI:10.5815/ijem.2026.05.10
Reference
[1]Hosseinzadeh, M., P. Khoshvaght, S. Sadeghi, P. Asghari, A. N. Varzeghani, M. Mohammadi, et al., (2024) “A Model for Epileptic Seizure Diagnosis Using the Combination of Ensemble Learning and Deep Learning,” IEEE Access.
[2]Nhu, D., M. Janmohamed, L. Shakhatreh, O. Gonen, P. Perucca, A. Gilligan, et al., (2023) “Automated Interictal Epileptiform Discharge Detection from Scalp EEG Using Scalable Time-Series Classification Approaches,” International Journal of Neural Systems 33(1), 2350001.
[3]Lytton, W. W., (2008) “Computer Modelling of Epilepsy,” Nature Reviews Neuroscience 9(8), 626–637.
[4]Zamanzadeh Darban, Z., G. I. Webb, S. Pan, C. Aggarwal, and M. Salehi, (2024) “Deep Learning for Time Series Anomaly Detection: A Survey,” ACM Computing Surveys 57(1), 1–42.
[5]Siripurapu, S., N. K. Darimireddy, A. Chehri, B. Sridhar, and A. V. Paramkusam, (2023) “Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review—Part I (AI, Big Data, Blockchain, Open-Source Technologies, and Cloud Computing),” Electronics 12(3), 750.
[6]Harun-Ur-Rashid. (2017). Epileptic Seizure Recognition Dataset. Kaggle. https://www.kaggle.com/datasets/harunshimanto/epileptic-seizure-recognition.
[7]Magnuson, J., F. Leonessa, and G. S. Ling, (2012) “Neuropathology of Explosive Blast Traumatic Brain Injury,” Current Neurology and Neuroscience Reports 12(5), 570–579.
[8]Kode, H., K. Elleithy, and L. Almazaydeh, (2024) “Epileptic Seizure Detection in EEG Signals Using Machine Learning and Deep Learning Techniques,” IEEE Access 12, 80657–80668.
[9]Mohammadi Foumani, N., L. Miller, C. W. Tan, G. I. Webb, G. Forestier, and M. Salehi, (2024) “Deep Learning for Time Series Classification and Extrinsic Regression: A Current Survey,” ACM Computing Surveys 56(9), 1–45.
[10]Liu, R., S. Wang, F. Tian, and L. Yi, (2025) “SIR-3DCNN: A Framework of Multivariate Time Series Classification for Lung Cancer Detection,” IEEE Transactions on Instrumentation and Measurement.
[11]Jahan, S., F. Nowsheen, M. M. Antik, M. S. Rahman, M. S. Kaiser, A. S. Hosen, and I. H. Ra, (2023) “AI-Based Epileptic Seizure Detection and Prediction in Internet of Healthcare Things: A Systematic Review,” IEEE Access 11, 30690–30725.
[12]Chen, R., X. Yan, G. Xiao, Y. Hou, and S. Zhou, (2025) “Trend and Order Features for Semi-Supervised Time-Series Classification via Multitask Learning,” IEEE Transactions on Neural Networks and Learning Systems.
[13]Tzallas, A. T., M. G. Tsipouras, D. G. Tsalikakis, E. C. Karvounis, L. Astrakas, S. Konitsiotis, and M. Tzaphlidou, (2012) “Automated Epileptic Seizure Detection Methods: A Review Study,” Epilepsy—Histological, Electroencephalographic and Psychological Aspects, 75–98.
[14]Shoeibi, A., M. Khodatars, N. Ghassemi, M. Jafari, P. Moridian, R. Alizadehsani, et al., (2021) “Epileptic Seizures Detection Using Deep Learning Techniques: A Review,” International Journal of Environmental Research and Public Health 18(11), 5780.
[15]Siddiqui, M. K., R. Morales-Menendez, X. Huang, and N. Hussain, (2020) “A Review of Epileptic Seizure Detection Using Machine Learning Classifiers,” Brain Informatics 7(1), 5.
[16]Vidyaratne, L. S., and K. M. Iftekharuddin, (2017) “Real-Time Epileptic Seizure Detection Using EEG,” IEEE Transactions on Neural Systems and Rehabilitation Engineering 25(11), 2146–2156.
[17]Tran, L. V., H. M. Tran, T. M. Le, T. T. Huynh, H. T. Tran, and S. V. Dao, (2022) “Application of Machine Learning in Epileptic Seizure Detection,” Diagnostics 12(11), 2879.
[18]Yu, S., R. El Atrache, J. Tang, M. Jackson, A. Makarucha, S. Cantley, et al., (2023) “Artificial Intelligence-Enhanced Epileptic Seizure Detection by Wearables,” Epilepsia 64(12), 3213–3226.
[19]Paul, Y., (2018) “Various Epileptic Seizure Detection Techniques Using Biomedical Signals: A Review,” Brain Informatics 5(2), 6.
[20]Sadati, N., H. R. Mohseni, and A. Maghsoudi, (2006) “Epileptic Seizure Detection Using Neural Fuzzy Networks,” Proceedings of the 2006 IEEE International Conference on Fuzzy Systems, Vancouver, Canada, 596–600.
[21]Guo, L., D. Rivero, J. Dorado, J. R. Rabuñal, and A. Pazos, (2010) “Automatic Epileptic Seizure Detection in EEGs Based on Line Length Feature and Artificial Neural Networks,” Journal of Neuroscience Methods 191(1), 101–109.
[22]Andrzejak, R. G., Lehnertz, K., Rieke, C., Mormann, F., 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. https://doi.org/10.1103/PhysRevE.64.061907.
[23]Lawhern, V. J., A. J. Solon, N. R. Waytowich, S. M. Gordon, C. P. Hung, and B. J. Lance, (2018) “EEGNet: A Compact Convolutional Neural Network for EEG-Based Brain–Computer Interfaces,” Journal of Neural Engineering 15(5), 056013.
[24]Dempster, A., F. Petitjean, and G. I. Webb, (2020) “ROCKET: Exceptionally Fast and Accurate Time Series Classification Using Random Convolutional Kernels,” Data Mining and Knowledge Discovery 34(5), 1454–1495.
[25]Dempster, A., D. F. Schmidt, and G. I. Webb, (2021) “MiniRocket: A Very Fast (Almost) Deterministic Transform for Time Series Classification,” Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 248–257.
[26]Tan, C. W., A. Dempster, C. Bergmeir, and G. I. Webb, (2022) “MultiRocket: Multiple Pooling Operators and Transformations for Fast and Effective Time Series Classification,” Data Mining and Knowledge Discovery 36(5), 1623–1646.