Enhanced Epileptic Seizure Detection Using a Hybrid CNN–LSTM Model on Eight Bipolar EEG Channels
Year: 2025
Type: article
Abstract: Automated detection of epileptic seizures from EEG recordings is critical for patient monitoring and early intervention.We propose a hybrid Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM) architecture that ingests eight bipolar EEG channels (C3-P3, P3-O1, P4-O2, P7-O1, P7-T7, T8-P8-0, T8-P8-1, and FP1-F7) to detect seizure events.On the CHB-MIT scalp-EEG dataset (49 999 samples), our model achieves 97.92 % test accuracy, 93.29 % precision, 85.33 % recall, 89.14 % F1-score, and an AUC of 0.9807.Cross-validation yields comparable metrics.We also deliver an interactive Streamlit web app for real-time inference.
Source: IJARCCE
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: diamond
OpenAlex ID: W4410443387