Preictal period optimization for deep learning-based epileptic seizure prediction

Abstract Objective: Accurate seizure prediction could prove critical for improving patient safety and quality of life in drug-resistant epilepsy. While deep learning-based approaches have shown promising performance using scalp electroencephalogram (EEG) signals, the incomplete understanding and variability of the preictal state imposes challenges in identifying the optimal preictal period (OPP) for labeling the EEG segments. This study introduces novel measures to capture model behavior under different preictal definitions and proposes a data-driven methodology to identify the OPP. &#xD;&#xD;Approach: We employed a competent subject-specific CNN-Transformer model (Area Under the Curve [AUC] of 99.35\% and F1-score of 97.46\%) to accurately detect preictal EEG segments using the open-access CHB-MIT dataset. To capture the temporal dynamics of the model's predictions, we fitted a sigmoidal curve to the model outputs obtained from uninterrupted multi-hour EEG recordings prior to seizure onset. From this fitted curve, we derived key performance measures reflecting the timing of predictions, including classifier convergence, average error, output stability, and the transition between interictal and preictal states. These measures were then combined to synthesize the Continuous Input-Output Performance Ratio (CIOPR), a novel metric designed to suggest the OPP for each patient.&#xD;&#xD;Significance: The newly developed metrics demonstrate that varying the preictal period significantly (p<0.001) impacts the timing of predictions in ways not captured by conventional accuracy-related metrics. Understanding this impact is essential for developing intelligent systems tailored to individual patient needs and for underlining practical limitations in detecting the preictal period in real-world clinical applications.