Title: Patient-specific approach for automated epileptic seizure state detection based on deep learning

Authors: Vibha Patel; Dharmendra Bhatti; Amit Ganatra

Addresses: Department of Computer Engineering, Chhotubhai Gopalbhai Patel Institute of Technology, Uka Tarsadia University, Bardoli – 394601, Gujarat, India ' Uka Tarsadia University, Bardoli – 394601, Gujarat, India ' Parul University, Waghodia, Vadodara – 391760, Gujarat, India

Abstract: Epilepsy is a chronic neurological disorder that occurs due to irregular brain activities. An automated approach to detect the epileptic seizure state from EEG recordings is highly desirable as the manual approach is exhausting, time-consuming, and error-prone. This work presents a hybrid 1D-CNN + stacked-LSTM model for an end-to-end, patient-specific EEG-based epileptic seizure state detection. The proposed work was tested on two datasets: CHB-MIT scalp EEG dataset and Siena scalp EEG dataset. It achieved highest result of 97.07% accuracy, 97.80% sensitivity, 97.07% specificity, 0.0293 FPR, and 0.99 AUC values on CHB-MIT dataset and 97.83% accuracy, 98.75% sensitivity, 97.82% specificity, 0.0218 FPR, and 0.99 AUC values on Siena scalp EEG dataset. The results obtained were compared with latest patient-specific seizure state detection methods. The proposed model achieved best patient-specific results despite the challenges of varying channels, recording duration, and seizure intervals.

Keywords: machine learning; deep learning; epilepsy; seizures; EEG.

DOI: 10.1504/IJBET.2026.153217

International Journal of Biomedical Engineering and Technology, 2026 Vol.50 No.3, pp.271 - 289

Received: 12 Mar 2025
Accepted: 03 Jun 2025
Published online: 29 Apr 2026 *

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