Epileptic seizure detection from EEG signals using ensemble of type-2 neuro-fuzzy inference systems with GWO-optimized fuzzy rules.

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

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.

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

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.

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.

Next-Generation Deep Learning Approaches for Accurate Epilepsy Detection from EEG Signals

Epilepsy is neurological disorder which is a result of abnormal brain activity which causes repetitive seizures. The analysis of electroencephalogram (EEG) is vital in determining the pattern of epileptic and facilitating clinical diagnosis. Manual interpretation of EEG signals is very cumbersome and time consuming however, because of the high-dimensional and time varying nature of the brain signals. An automated epilepsy detection (EEG) framework is trained based on deep learning. It is a signal preprocessing, time frequency transformation, hierarchical feature extraction based on a hybrid neural architecture that encodes spatial and temporal EEG features. This is followed by the classification of the learned representations with the aim of obtaining the epileptic and non-epileptic brain activity patterns. The experimental assessment shows a better performance than the traditional procedures. The currently proposed model is significantly more accurate (96.7%), sensitive (96.0%), and specific (96.2%), as compared to classical machine learning models like support vector machines (91.2% accuracy) and random forest models (92.6% accuracy). Such results suggest that the improvement in performance of these techniques will be about 4-5 percent compared to the use of conventional techniques. The framework has a high potential of aiding in stable and automatic diagnosis of epilepsy in clinical settings.

Optimization-Driven EEG-Based Seizure Detection in the Healthcare Internet of Things.

OBJECTIVE This work proposes an automated system for the detection of epileptic seizures based on electroencephalogram (EEG) signals, integrating numerous metaheuristic optimization algorithms. This study aims to improve classification accuracy and computational efficiency by optimizing feature selection and classification in a machine learning-based detection system. METHODS The four feature extraction strategies were employed:(i) the Discrete Wavelet Transform (DWT), (ii) the DWT with the Hjorth parameters, (iii) the DWT with the statistical features, and (iv) the DWT with the statistical and Hjorth parameters. The metaheuristic- based optimization methods, Particle Swarm Optimization (PSO), Ant Colony Optimization (ACO), Bee Algorithm (BA), and Genetic Algorithm (GA) were used to select the optimal feature from each feature set. Optimized features were then classified using a Support Vector Machine (SVM) classifier. RESULTS DWT + PSO + SVM model showed the highest level of classification of 97.8%, which is significantly higher compared to the other combinations of feature extraction and optimization. The feature optimization using Genetic Algorithms showed better computational efficiency, whereas ACO and BA yielded performance improvement within a given feature configuration. DISCUSSION The better performance of the meta-heuristic-optimized models, especially the DWT-PSO hybrid, demonstrates the effectiveness and robustness of the proposed model for automated seizure detection. These findings demonstrate the appropriateness of meta-heuristic optimization for dealing with the complex feature space of EEG and enhancing detection reliability. The results are particularly meaningful to real-time seizure monitoring systems in resource-limited systems, e.g., Healthcare Internet of Medical Things (IoMT) systems. CONCLUSION The findings indicate the high potential of meta-heuristic optimization tools for improving the accuracy of epileptic seizure detection and the overall performance of the system. The suggested architecture provides a clear pathway for developing effective, reliable, and real- time EEG-based seizure-detection systems.

Enhancing epileptic seizure detection via augmentation of electroencephalography (EEG) signals using a single-transistor chaotic oscillator