Semi-Supervised Anomaly Detection Pipeline for SOZ Localization Using Ictal-Related Chirp
This provides a complementary method for SOZ localization in epilepsy surgery planning, particularly for patients with successful outcomes, but it is incremental as it builds on existing time-frequency and outlier detection techniques.
The study tackled the problem of localizing seizure onset zones (SOZs) in epilepsy patients by developing a semi-supervised anomaly detection pipeline using ictal-related chirp events, achieving high precision (mean up to 0.903) in patients with successful surgical outcomes.
This study presents a quantitative framework for evaluating the spatial concordance between clinically defined seizure onset zones (SOZs) and statistically anomalous channels identified through time-frequency analysis of chirp events. The proposed pipeline employs a two-step methodology: (1) Unsupervised Outlier Detection, where Local Outlier Factor (LOF) analysis with adaptive neighborhood selection identifies anomalous channels based on spectro-temporal features of chirp (Onset frequency, offset frequency, and temporal duration); and (2) Spatial Correlation Analysis, which computes both exact co-occurrence metrics and weighted index similarity, incorporating hemispheric congruence and electrode proximity. Key findings demonstrate that the LOF-based approach (N neighbors=20, contamination=0.2) effectively detects outliers, with index matching (weighted by channel proximity) outperforming exact matching in SOZ localization. Performance metrics (precision, recall, F1) were highest for seizure-free patients (Index Precision mean: 0.903) and those with successful surgical outcomes (Index Precision mean: 0.865), whereas failure cases exhibited lower concordance (Index Precision mean: 0.460). The key takeaway is that chirp-based outlier detection, combined with weighted spatial metrics, provides a complementary method for SOZ localization, particularly in patients with successful surgical outcomes.