Patient-specific long-term seizure prediction via multi-model classification

<i>Objective.</i>Most existing seizure prediction approaches rely on cohort-based models or assume a single model suffices per patient, overlooking clinical and electrophysiological variability across seizures. This study aims to overcome these limitations by introducing a subject-specific seizure prediction framework that models intra-subject heterogeneity by identifying and clustering seizure-specific preictal patterns using<i>long-term</i>intracranial EEG (iEEG) recordings collected over a one to two week duration.<i>Approach</i>: Absolute, relative, and ratio power spectral density features are extracted from twelve frequency bands, and the minimum uncertainty and sample elimination algorithm is used for unsupervised feature selection on a per-seizure basis. Weighted aggregation is then applied to form seizure-specific feature sets. Seizures are grouped into clusters based on feature similarity, and separate classifiers are trained for each cluster. Model predictions are combined using a grid optimized<i>k</i>-of-<i>N</i>voting strategy. Evaluation is conducted on long-term iEEG recordings from ten patients using cross-validation across seizure-containing sessions.<i>Main results.</i>When clustering is applied, the mean sensitivity across subjects is improved from 89.17% to 98.54%, while the mean FPR is reduced from 1.15/day to 0.62/day. Additionally, the median number of features required per subject decreased from 22 to 14, reflecting a 36.4% reduction in model complexity. Finally, in 72.5% of subject-folds, the number of algorithm-identified clusters equaled or exceeded the clinically annotated seizure types, with a linear trend indicating latent electrophysiological variability beyond clinical labels.<i>Significance.</i>These findings highlight the value of modeling seizure diversity within individuals and support the development of more personalized and interpretable seizure forecasting systems.