Jul 2026· 2026 11th International Conference on Applying New Technology in Green Buildings (ATiGB)· pp. 1854-1859· 0 citations· 22 references
Abstract
The automated recognition of epileptic events from long-term electroencephalogram (EEG) monitoring remains a clinically significant challenge, demanding high sensitivity and computational efficiency for real-world deployment. Spiking Neural Networks (SNNs) offer a biologically plausible and energy-efficient alternative to conventional deep learning. However, existing SNN architectures for seizure detection either lack spatial information across electrode channels or fail to model global dependencies across temporal positions. In this paper, we propose a dual-branch Spatio-Temporal Spiking Transformer (ST-Spikformer) to detect epileptic seizure segments from scalp long-term electroencephalogram (EEG) recordings. We evaluated on the CHB-MIT Scalp EEG dataset, which contains 23 recording cases. Experimental results demonstrate high performance against published SNN baselines, with an accuracy of 96.2%, sensitivity of 90%, and specificity of 99%. Our implementation is available at https://github.com/ngochien108/ST-Spikformer.
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
Prediction of epileptic seizures using Electroencephalography (EEG) is an important research area, as it can provide early warning and enhance patient safety in neurological healthcare. Deep learning models such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Vision Transformers and Spiking N...
P. Sujatha, C. Rekha, P. Naresh et al.· 2026 International Conferenc...· 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
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.
Edgar H. Ayala-Britez, Lucas Frutos, Diego P. Pinto-Roa et al.· Machine Learning and Knowled...· 0 citations
A compact composite feature termed the Seizure Intensity Index (SII) together with an extended representation incorporating additional theta and alpha band descriptors is proposed together with an extended representation incorporating additional theta and alpha band descriptors for cross-patient seizure detection.
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.
