Deep CNN ensemble framework for early detection of epileptic seizures using STFT-derived EEG features

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

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.

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.

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.

Pre-Ictal Seizure Prediction from EEG Using an Attention-Augmented Temporal Learning Framework

There are approximately 50 million people worldwide living with epilepsy, highlighting the need for strong early warning systems to enable prompt clinical management. Despite the high performance of recently developed deep learning models based on Convolutional Neural Networks (CNNs) and Bidirectional Long Short-Term Memory (Bi-LSTM/GRU) networks, they are highly sensitive to ictal features and are not very effective in detecting the subtle temporal transitions leading to seizure onset. This article introduces an attention-improved deep learning model to predict pre-ictal seizures using EEG signals. The suggested model combines CNN-based feature extraction, Bi-LSTM/GRU temporal sequence modelling, and a learnable temporal attention mechanism to identify the early neural dynamics before the onset of seizures. The experimental dataset was constructed from four publicly available EEG repositories following the proposed temporal labeling strategy and comprises EEG recordings from 25 selected patients, 243 annotated seizure events, and 2,847 hours of continuous EEG recordings, using a patient-wise stratified 5-fold cross-validation protocol. Experimental evaluation demonstrates that the proposed framework achieves a predictive accuracy of 93% (±1.2%), sensitivity of 90% (±1.4%), specificity of 90% (±1.1%), an AUC-ROC of 0.93 (±0.012), a PR-AUC of 0.961 (±0.009), and a low false alarm rate of 0.12 ± 0.015 per hour. To confirm statistical reliability, all reported metrics are validated across 10 independent runs and supplemented with 95% confidence intervals and paired Wilcoxon signed-rank tests (p < 0.05) against all baselines. Attention weight analysis verifies that the model selectively targets temporally informative pre-ictal EEG regions and improves clinical interpretability.

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.