Seizure Classification Using CNN and STFT: A Deep Learning Approach

The present research integrates Convolutional Neural Networks (CNN) and Short-Time Fourier Transform (STFT) to offer an accurate method for identifying seizures as focal or generalized using EEG signals. To ensure high-quality inputs, the pipeline starts with signal preprocessing, which includes noise reduction, artifact correction, and normalization. STFT, which transforms EEG signals into spectrograms and captures the temporal and spectral information necessary for differentiating seizure types, is used for feature extraction. Preprocessing techniques like filtering and normalization ensure that the incoming EEG signals are readable and suitable for analysis. The CNN can identify complex patterns that distinguish between focal and generalized seizures by using the spectrograms generated by STFT as input features. The CNN's multi-layered architecture provides accurate and reliable classification by identifying these subtle changes, which is essential for assisting neurologists in patient diagnosis and treatment plan modification. This method provides a scalable and dependable real-time seizure categorization solution that supports better epilepsy management, individualized treatment planning, and improved diagnosis.