Jun 2026· International journal of electrical and electronics research· pp. 604· 0 citations· 17 references
TL;DR
Findings underscore the potential of higher-order statistical descriptors, particularly kurtosis, for EEG-based epileptic seizure detection under a controlled benchmark setting and underscore the potential of classical machine learning classifiers to distinguish normal and epileptic EEG signals.
Abstract
Epileptic seizure detection from EEG signals remains challenging due to their non-stationary and complex nature. This study presents a comparative analysis of Discrete Wavelet Transform (DWT)-based feature extraction combined with classical machine learning classifiers (SVM, KNN, and MLP) to distinguish normal and epileptic EEG signals. Using the publicly available Bonn University dataset (Sets A and E), EEG signals were decomposed using the Daubechies-4 (db4) wavelet into five decomposition levels corresponding to standard frequency bands (Delta, Theta, Alpha, Beta, Gamma). Seven statistical features—energy, mean amplitude, standard deviation, Shannon entropy, relative wavelet energy (RWE), kurtosis, and skewness—were extracted from each sub-band. A stratified 10-fold cross-validation with a leakage-controlled record-level partitioning strategy was implemented to reduce optimistic bias. Since subject-level identifiers are unavailable in the public Bonn dataset, the validation was designed to avoid re-splitting individual EEG records across training and testing stages. Results demonstrate that kurtosis-based features consistently achieve the highest accuracy (99.8% ± 0.3) across all classifiers, significantly outperforming other features (p < 0.01). These findings underscore the potential of higher-order statistical descriptors, particularly kurtosis, for EEG-based epileptic seizure detection under a controlled benchmark setting.
This paper presents a comparative study for epilepsy monitoring using EEG signals along two main axes. The first axis consists of comparing the performance of the differentiation technique, which is known to be very important for the study of non-stationarity, with wavelet transform, which is widely used for detecting different brain rhythms. The second part aims to compare the performance of different machine learning algorithms, including k-Nearest Neighbors (k-NN), Decision Trees, Random Forest, and Support Vector Machines (SVM). Used features are statistical and higher order statistics characteristics such as mean, standard deviation, median, Min-Max, Kurtosis and Skewness. We tested our approach on publicly available and widely used datasets in the literature, namely the University of Bonn dataset and the Bern-Barcelona dataset. The experimental results demonstrate the effectiveness of the differentiation method as an important tool for EEG preprocessing, leading to very high performance.
Ines Bouzouita, Zayneb Brari, S. Belghith· International Conference on...· 0 citations
The epileptic seizure (ES) is one of the most prominent neurological conditions, whose detection and classification from the electroencephalogram (EEG) signals is crucial for effective diagnosis of seizures, thereby eliminating the detrimental effects associated with it. However, the development of an automated ES detection system is hindered by the non-stationary, non-linear, and high-dimensional nature of the EEG signals, compounded by noise contamination and inter-subject variability. To address these challenges, this paper proposes an automated ES detection framework based on the De-mixing Multivariate Variational Mode Decomposition (D-MVMD) integrated with the Bayesian Optimized Support Vector Machine (BO-SVM). The D-MVMD decomposes multichannel EEG signals into band-limited intrinsic mode functions (BIMFs) while alleviating the correlation between corresponding modes through an ensemble correlation coefficient, while preserving the seizure characteristics from contamination. Multi-domain features capturing temporal, spectral, and non-linear dynamics of seizure activity are then extracted from the de-mixed BIMFs. ReliefF-ranked random forest-based feature selection is employed to find discriminative features, which are subsequently classified using the optimally tuned BO-SVM classifier. Experimental evaluation on the CHB-MIT demonstrates superior performance, with an accuracy of 98.52%, precision of 98.67%, sensitivity of 98.54%, specificity of 98.54%, and F1 score of 0.98. The model is also evaluated on the Siena dataset to assess its robustness across recording sessions of the same patient. Further, it is analyzed with other state-of-the-art methods, confirming its superior mode separation and enhanced seizure detection. Hence, this developed model proves itself to be an effective and robust model for detecting seizures using the multichannel EEG analysis.
T. V. Manju, M. Hota· IEEE journal of biomedical a...· 0 citations
A Deep Hybrid Neural Network framework that combines Convolutional Neural Networks (CNN) with the Aquila Optimizer (AO) for the automatic detection of epileptic seizures utilizing EEG data in MATLAB is introduced.
Swati Chowdhuri, Tiyasha Mondal· International Journal of Eng...· 0 citations
A compact composite feature termed the Seizure Intensity Index (SII) together with an extended representation incorporating additional theta and alpha band descriptors is proposed together with an extended representation incorporating additional theta and alpha band descriptors for cross-patient seizure detection.
This study systematically introduces and evaluates 25 less-explored time-domain features, 13 of which have no documented precedent as classification features in scalp EEG seizure detection, against 25 classical features and their 50-feature combination.
Edgar H. Ayala-Britez, Lucas Frutos, D. Pinto-Roa et al.· Machine Learning and Knowled...· 0 citations
A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.
S. Edoigiawerie, J. Henry, B. Beaulieu-Jones et al.· medRxiv· 0 citations
