Purpose: Epilepsy is a chronic neurological disorder that affects more than 50 million people worldwide, where early detection through EEG signal analysis is crucial for proper management. However, the quality of EEG signals is often affected by noise and artifacts, which can lead to diagnostic errors of up to 30% in the early stages. This study aims to develop an EEG signal preprocessing method to improve the classification performance of epileptic symptoms through preprocessing, segmentation, and seizure interval analysis approaches. Methods: The preprocessing stage involved applying a 50 Hz notch filter and a 0.5–60 Hz bandpass filter. The contribution of this work is in the development of hybrid segmentation based on frequency and amplitude analysis, while seizure intervals were identified using distances criteria between consecutive spikes detected on signals. The method was tested using the CHB-MIT dataset consisting of 23 EEG channels. Result: The results showed that the system successfully identified seizure segments with an average accuracy of 62.09%, and 9 out of 23 channels achieved accuracies above 70%. Channels Ch08 (86.60%), Ch09 (86.36%), and Ch19 (80.51%) achieved the highest accuracies. The results also showed high specificity(99.85%) and low False Positive rate(0.15%) indicating the system’s effectiveness to reduce falase positive. Novelty: This method proved effective in detecting epileptiform activity and shows potential as an EEG-based early detection tool for epilepsy, although further optimization is needed to improve accuracy on channels with low signal-to-noise ratio (SNR).
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