Comparative Analysis of DWT-Based EEG Feature Extraction Using Machine Learning Models for Epileptic Seizure Detection

DPML: Derivative-Based EEG Preprocessing for Enhanced Epileptic Seizure Detection Using Machine Learning

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.

An Automated Multivariate EEG Signal Analysis Framework for Seizure Detection Using D-MVMD and BO-SVM.

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.

Deep Hybrid Neural Network for Automated Epileptic Seizure Detection from EEG Signals using MAT-LAB

Epilepsy is a long-term neurological disorder marked by recurring seizures resulting from irregular neuronal activity in the brain. Prompt and precise identification of epileptic events from electroencephalogram (EEG) signals is essential for successful clinical diagnosis. This study introduces 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. The suggested system initially converts EEG signals into time–frequency spectrograms through Short-Time Fourier Transform (STFT), allowing the CNN to capture advanced spatial–spectral characteristics. The AO algorithm further improves these features by tuning hyperparameters and choosing the most distinguished fea-ture subsets, thereby boosting classification accuracy and decreasing computational overhead. The Bonn University EEG dataset was used to evaluate the model through a 5-fold cross-validation method, attaining an average accuracy of 95.62%, where per-class sensitivity and specificity surpassed 97%. Comparative evaluation showed that the CNN–AO hybrid sur-passed traditional classifiers in terms of accuracy and convergence reliability. These findings demonstrate the effectiveness of the proposed hybrid framework for automated epileptic seizure detection and suggest its potential suitability for future real-time and wearable healthcare applications following further deployment-oriented validation.

Composite EEG biomarker modeling and energy–accuracy trade-off analysis for multi-patient seizure detection

Reliable cross-patient seizure detection remains challenging because EEG characteristics vary considerably between patients. To address this problem, we proposed a compact composite feature termed the Seizure Intensity Index (SII) together with an extended representation incorporating additional theta and alpha band descriptors. The proposed feature representations were evaluated using Logistic Regression, Artificial Neural Network (ANN), and Spiking Neural Network (SNN) on the CHB-MIT and TUH EEG Epilepsy Corpus datasets. Spectral parameterization analysis showed that the proposed features primarily reflected localized oscillatory activity rather than global spectral shifts. The ANN achieved accuracies of 90.40% under random-split validation and 56.58% under CHB-MIT Leave-One-Patient-Out (LOPO) validation, indicating reduced cross-patient generalization. Logistic Regression achieved CHB-MIT LOPO accuracies of 72.83% using the baseline SII representation and 74.50% using the extended representation, while the SNN improved from 64.34% to 68.01%. During external validation on the TUH EEG Epilepsy Corpus, the SNN achieved the highest accuracy under the extended feature representation (90.78%). Although the extended representation improved performance, substantial cross-patient variability remained.

Real-Time Epileptic Seizure Detection from Raw EEG Using Classical Machine Learning and Time-Domain Feature

Automated detection of epileptic seizures from electroencephalogram (EEG) recordings is essential for timely clinical intervention and long-term patient monitoring. Deep learning achieves high accuracy, but its limited interpretability and computational demands restrict deployment in resource-constrained, real-time clinical environments; furthermore, classical machine learning studies have concentrated on a narrow group of well-known temporal features. 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. Raw, unfiltered recordings from the CHB-MIT database were segmented into 10-second windows, and seven classical classifiers were optimized with GridSearchCV under subject-wise StratifiedGroupKFold cross-validation, with the data partitioned at the patient level such that no patient appeared in both the training and test sets. The multilayer perceptron trained on the combined 50-feature set performed best (accuracy 86.59%, F1-score 86.35%), exceeding the classical features alone by 5.1 and 4.6 percentage points, respectively; the less-explored features alone remained competitive (84.63%, 84.67%). SHAP analysis identified the exponent of the detrended fluctuation analysis (DFA)—a long-range, nonlinear measure of temporal correlation—as the most influential predictor. The curated feature set, its rigorous subject-level validation, and its interpretability provide a reproducible and computationally efficient foundation for future clinical deployment.

Neonatal Seizure Detection Using Combined aEEG and Compressed Spectral Array Features: A Machine-Learning Proof-of-Concept Study