Spike-wave discharges (SWD) are crucial biomarkers in the diagnosis and monitoring of neurological disorders such as epilepsy. Accurate classification of SWD is essential for effective clinical interventions and improving patient outcomes. This study presents a novel approach for classifying spike-wave discharges using the Short-Time Fourier Transform (STFT). By leveraging STFT's capability to analyze non-stationary signals, we extract time-frequency features from EEG recordings to accurately distinguish SWD from other brain activities. The extracted features are then classified using machine learning algorithms, providing high accuracy in identifying SWD events. Performance evaluation demonstrates that the proposed STFT-based method offers significant improvements in classification accuracy and computational efficiency compared to traditional time-domain analysis. The study's findings highlight the potential of STFT in real-time applications for automated seizure detection, contributing to advancements in neurological disorder diagnostics.

Spike-wave discharge, Short-Time Fourier Transform, EEG classification

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