Three-Phase Seizure Segmentation in Stereotactic EEG Using Envelope-Based Multivariate Changepoint Analysis

Accurate segmentation of seizure phases in intracranial EEG is essential for characterizing seizure dynamics and supporting presurgical evaluation in drug-resistant focal epilepsy. This study examines whether a semi-supervised changepoint detection framework can reliably delineate ictal onset, intra-ictal transition, and seizure termination.

A three-phase segmentation pipeline integrates multivariate envelope-based features, including root mean square amplitude, relative bandpower in the theta (4-8 Hz), alpha (8-13 Hz), beta (13-30 Hz), and gamma (30-80 Hz) bands, line length, and spectral entropy, with the Pruned Exact Linear Time algorithm. Features were extracted from sliding windows whose lengths and phase-specific weights were optimized using nested leave-one-subject-out cross-validation with Optuna. To ensure length invariance, analysis windows were randomly extended by 5-30 s before seizure onset and after seizure termination using real pre- and post-ictal data. Performance was evaluated on 179 seizure-onset-zone bipolar channels across 32 seizures from 10 patients.

Mean absolute errors were $4.19\pm 2.69$ s for seizure onset, $6.93\pm 5.75$ s for intra-ictal transition, and $3.82\pm 4.24$ s for seizure termination. Detection accuracies within $\pm 5$ s were 71.6% for onset, 60.0% for transition, and 75.0% for termination. Phase-specific feature importance analysis revealed distinct and evolving contributions of amplitude-, spectral-, and complexity-based measures across seizure phases.

The proposed framework achieves temporal precision comparable to reported inter-rater reliability (Cohen's $\kappa =0.35$ -0.69) and provides an interpretable, data-driven approach for comprehensive seizure phase characterization, with potential utility in clinical decision-making.