A hybrid deep learning architecture for patient-independent epileptic seizure identification that integrates a Hierarchical Temporal Separable Convolutional Network, a Dual-Stage Bidirectional Recurrent Neural Network, and a Multi-Head Attention Mechanism is proposed, enabling effective extraction of spatial, temporal, and contextual features from non-stationary EEG signals while addressing inter-patient variability.
An attention-improved deep learning model is introduced to predict pre-ictal seizures using EEG signals and attention weight analysis verifies that the model selectively targets temporally informative pre-ictal EEG regions and improves clinical interpretability.
Maleka Anjum, Shubhangi D. C.· Journal of Innovative Image...· 0 citations
The proposed hybrid model 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 and assist clinical medical decisions.
Xingran Wang, Ting-Hao Gong, Xue-Jia Li et al.· Frontiers in Neuroscience· 0 citations
The use of pre-trained models reduced the training time and resources required, and the unique application of the ensemble learning approach produces more robust and reliable results compared to individual deep learning models.
Unnati Chaurasia, Shilpa Sj, H. Pathak et al.· Discover Artificial Intellig...· 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
The study comes to the conclusion that the MSNetV2-DCNN model exhibits a reliable and effective technique for epileptic seizure identification, underscoring its potential for practical use in traffic management situations as well as medical diagnostics.
This work proposes BrainXNet, a novel multi-scale spectro-temporal attention framework that unifies local feature extraction, frequency-aware representation learning, and global temporal modeling within a single architecture and bridges the gap between high-performance experimental models and practical deployment in di...
Mostafa Gamal, Mustafa Abdel-Wanes· Scientific Reports· 0 citations
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