Impact of Artifact Removal from EEG Signals on Epileptic Seizure Detection

The aim of this study was to evaluate the impact of different artifact removal techniques on the performance of computerized epileptic seizure detection from EEG data. The desired outcome was to explore signal processing techniques that would yield the more optimal signal quality for enabling accurate identification of seizure events. Two popular artifact removal methods - wavelet transform and empirical mode decomposition were applied to clean pre-recorded EEG recordings with simulated artifacts. Technical measures and clinical detection accuracy were then used to compare the performance of each technique. Our findings indicate that empirical mode decomposition more effectively mitigated artifacts, achieving a higher signal-to-noise ratio of approximately 6dB compared to -4dB for wavelet transform. Seizure detection precision and sensitivity were also improved with empirical mode decomposition preprocessing, exceeding 80% for most metrics. This research highlights the significance of thorough artifact removal in facilitating automated seizure detection from EEG signals. By minimizing noise from extraneous sources, underlying ictal patterns can be better characterized, aiding epilepsy diagnosis and management. With ongoing refinement, such signal processing techniques show promise for augmenting clinical decision-making and improving care for people living with this condition.