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Researchers developed a weakly supervised machine learning framework to identify seizure onset zones in patients with drug-resistant epilepsy without requiring precise electrode-level annotations. The system uses multiple-instance learning with neural fragility sequences from 42 patients across 136 seizure epochs, achieving an area under the ROC curve of 0.828 at the seizure level and 0.944 at the patient level in predicting surgical outcomes. The framework performed comparably to existing methods while using only surgical resection status and patient outcomes as training labels rather than detailed electrode annotations.
Why it matters
This approach could improve surgical planning for epilepsy patients by identifying seizure onset zones without the need for labor-intensive expert annotation of individual electrodes. The weakly supervised method may enable more scalable analysis of intracranial recordings and potentially better surgical outcomes through improved target localization.
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⚠️ Preprint – Noch nicht peer-reviewed
Dieser Artikel wurde noch nicht von unabhängigen Experten begutachtet. Die Ergebnisse sind vorläufig und sollten mit Vorsicht interpretiert werden.
Accurate localization of the seizure onset zone (SOZ) is essential for planning resection or ablation in patients with focal drug-resistant epilepsy (DRE). However, the true SOZ cannot be directly observed during clinical evaluation, so precise electrode-level annotations are rarely available. To address this limitation, we developed a weakly supervised multiple-instance learning (MIL) framework in which labels were constructed from electrode resection status and postsurgical outcome. A shared gated recurrent unit (GRU) with temporal attention encoded each electrode’s peri-onset neural fragility sequence. And attention-based pooling integrated information across variable numbers of electrodes to estimate whether each electrode group contained SOZ-related evidence. The cohort comprised 42 patients with DRE from three datasets, including 29 with successful and 13 with failed postsurgical outcomes. These patients contributed 136 seizure epochs. The cohort was divided at the patient level into training, validation and held-out test sets containing 25, 8 and 9 patients, respectively, with all data from each patient retained in the same subset. Predictions from non-resected regions were evaluated at the seizure-epoch and patient levels to assess postsurgical outcome discrimination for individual seizures and after aggregation across seizures from the same patient, respectively. At the seizure-epoch level, the proposed framework achieved an area under the receiver operating characteristic (ROC) curve (AUC) of 0.828, with a 95% confidence interval (CI) of 0.511-1.000, and performed comparably to Manifold Oblique Random Forests (MORF). At the patient level, the framework achieved an AUC of 0.944, with a 95% CI of 0.677-1.000, and yielded a numerically higher AUC than MORF. However, DeLong’s test did not show a statistically significant difference between the methods, with a P value of 0.398. These findings support the feasibility of extracting clinically meaningful SOZ-related information from electrode-wise neural fragility sequences without precise electrode-level labels. Because the held-out test set included only nine patients and the CIs were wide, these preliminary findings require validation in larger independent cohorts.
Source: Weakly Supervised Multiple-Instance Learning for Seizure Onset Zone Identification