Two-Stage Approach With Combination of Outlier Detection Method and Deep Learning Enhances Automatic Epileptic Seizure Detection

Many approaches to automated epileptic seizure detection share a common challenge — the trade-off between recall and precision. This study aims to develop a novel approach for reducing false positive predictions in seizure detection tasks applied to real-world EEG recordings. We propose a multi-stage modeling framework, for which the novelty lies in combination of traditional machine learning outlier detection with state-of-the-art convolutional neural networks. Our dataset includes raw epileptic EEG data directly from the hospital. Continuous wavelet analysis is employed for EEG preprocessing and feature extraction. We evaluated the performance of the proposed two-stage algorithm, and it demonstrated a slight decrease in recall but a significant improvement in precision in comparison to machine-learning-only or neural-network-only algorithms. We hypothesize that this finding aligns well with our previous research and relates to the fundamental properties of epileptic EEG, including the extreme behavior of seizures. Finally, we propose a potential practical application of the developed approach within a clinical decision support system.