An Efficient Dual-Specialist Framework for Real-Time Epileptic Seizure Detection and Prediction

An Optimized Deep Learning Framework for Robust Epileptic Seizure Detection Using EEG Signals

A proposed method for detecting epileptic seizures from electroencephalogram data involves creating an optimal deep learning architecture that incorporates deep learning architectures, feature optimisation, and wavelet-based preprocessing.

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.

Epileptic Seizure Detection from EEG Signal Using Progressive Channel Selection and Deep Learning

This study investigates a novel Progressive Channel Selection (PCS) framework designed to identify and retain only the most informative EEG channels across patients, which provides a more effective trade-off between detection accuracy and channel efficiency.

A CNN-BiLSTM-GRU and attention-integrated hybrid network for epileptic seizure detection

Introduction Epilepsy is a common neurological disease, and accurate seizure detection is essential for clinical monitoring and scientific treatment. This study aims to construct an effective intelligent detection model to achieve precise automatic identification of epileptic EEG signals and assist clinical medical decisions. Methods To capture subtle local waveform variations and suppress redundant noise interference in EEG signals, this study adopts one-dimensional convolutional neural network (1D-CNN) layers for adaptive local feature extraction and a lightweight global temporal soft attention mechanism for critical feature enhancement. A hybrid classification model based on bidirectional long short-term memory (Bi-LSTM) and gated recurrent unit (GRU) is proposed for the binary classification of epileptic EEG signals. The synthetic minority oversampling technique (SMOTE) is applied only to the training data within each cross-validation fold to alleviate the class imbalance problem of EEG datasets. Results The proposed hybrid model achieves a binary classification accuracy of 99.23%, while delivering an especially balanced sensitivity (99.29%) and specificity (99.34%), with a difference (∆ Sens–Spec) of only 0.05%, verified on the public UCI epileptic seizure recognition data set. Discussion The CNN-Bi-LSTM-GRU and attention-integrated hybrid network can effectively distinguish seizure and non-seizure EEG signals. And a nearly equal sensitivity and specificity suggests robust and unbiased classification. Which is critical for clinical deployment. The proposed method achieves competitive performance compared with most recent mainstream algorithms, which can offer a potential automated detection reference to assist clinical analysis of epilepsy EEG signals.

Hierarchical temporal-separable convolutional-recurrent attention network for EEG-based epileptic seizure prediction

Diagnosis of Neurological Dysfunction from EEG Signals Using DuelQ-SeizureNet and Meta Black Ant Optimization for Epileptic Seizure Detection.

BACKGROUND Early diagnosis of neurological dysfunctions, particularly epilepsy, is vital for early intervention and improvement of patients' quality of life. However, traditional seizure detection techniques suffer from low detection accuracy, high false positive rate, and high computational complexity, making it difficult to effectively capture the complex spatiotemporal characteristics of electroencephalography (EEG) signals. Despite the significant improvements in seizure detection accuracy brought by deep learning techniques, the current models suffer from inaccuracies, limited adaptability, and inability to operate in real time. NEW METHOD To address these challenges, a new hybrid deep learning model based on one-dimensional Convolutional Neural Network (1D-CNN), Bidirectional Long Short-Term Memory (BiLSTM) network and Dueling Q-Learning is proposed to accurately classify epileptic seizures from EEG signals in an adaptive manner. In addition, a novel approach is proposed called Metaheuristic Based Adaptive Optimization (MBAO) to adaptively select an optimal temporal window size for the effective extraction of features, while minimizing the required information loss and computation burden. RESULTS and Comparison with existing methods: The proposed model has tested in various experiments conducted in a large number of benchmark datasets like CHB-MIT, Kaggle EEG Epileptic datasets etc. which justifies the effectiveness of the proposed model. DuelQ-SeizureNet has an accuracy of 99%, a precision of 96%, a recall (sensitivity) of 98%, a specificity of 99%, and an F1 score of 99% with a low execution time of 50ms in seizure prediction. CONCLUSIONS This proposed framework introduces a novel reinforcement learning assisted optimization approach in deep seizure detection architecture. It can operate with lower false detection rates (1.8%), higher area under the ROC curve (AUC) (0.995), and lower computational speed than the existing scheme, ensuring reliable real-time implementation.