Jul 2026· 2026 International Conference on Emerging Trends in Information, Communication & Systems (ICETICS)· pp. 1-6· 0 citations· 23 references
Abstract
Due to EEG overlap, brain tumour-induced seizures (BTIS) and primary epileptic seizures (ES) are difficult to distinguish. This paper introduces an AI-enhanced BCI model that uses hybrid deep learning architectures to differentiate accurately. We used CNNs for spatial feature extraction and Transformer-based attention mechanisms for temporal dependency modelling to decompose multi-channel EEG data using wavelet and Fourier methods. On a multi-centre dataset of 1,240 EEG sessions (500 subjects: 125 healthy controls, 187 epileptic, 188 tumour-induced seizure cases), our model had 96.7% sensitivity, 94.5% specificity, and 95.6% accuracy. The framework performed well across patient demographics and seizure types. Real-time lightweight architecture detection latency was 8.3 seconds with 0.62 false alarms per day. This study fills a clinical need by offering an automated, interpretable diagnostic tool that may improve seizure treatment and tumour-related neurological consequences.
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 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
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.
Moka Nanditha Varma· Journal of Intelligent Decis...· 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
One of the neurological conditions that affects the emotional and psychological condition of individuals is known as Epilepsy. Managing this disorder is very challenging as the focal seizures begin in specific brain regions and evolve into generalized forms. The fundamental method for seizure identification is the anal...
Maneesh Kumar, Rakesh Kumar, Santosh Kumar· International Journal of Int...· 0 citations
Epilepsy is a neurological disorder that affects many individuals worldwide. Accurate seizure classification using electroencephalogram (EEG) signals is essential for supporting epilepsy diagnosis. However, EEG signals are often noisy, rapidly varying, and characterized by patterns occurring at multiple temporal scales...
V. Gurrala, Helan Satish, MP. Sricharan Reddy· International Conference on...· 0 citations
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