An Efficient Deep Learning Framework for Automated Epileptic Seizure Detection: Toward Scalable and Clinically Applicable Solutions

ABSTRACT In this study, we present an efficient epileptic seizure detection framework driven by a graph convolutional neural network (GCNN). Unlike conventional methods that primarily rely on local features or complex feature engineering, our GCNN‐based approach explicitly encodes the spatial dependencies among electroencephalogram (EEG) electrodes, thereby capturing more comprehensive spatiotemporal features. A minimal preprocessing pipeline, consisting only of bandpass filtering and segmenting, reduces system complexity and computational overhead. On the CHB‐MIT scalp EEG database, our method achieved an average accuracy of 98.64%, sensitivity of 99.49%, and specificity of 98.64% at the segment‐based level and sensitivity of 96.81% with FDR of 0.27/h at the event‐based level. On the SH‐SDU database we collected, the method yielded segment‐based accuracy of 95.23%, sensitivity of 92.42%, and specificity of 95.25%, along with event‐based sensitivity of 94.11%. The average testing time for 1 h of multi‐channel EEG signals is 3.89 s. These excellent results and low‐computation design make the framework especially suited for clinical applications, advancing EEG‐based epilepsy diagnostics and improving patient outcomes.