Graph representations of iEEG data for seizure detection with graph neural networks
Bibliographic record
Abstract
ABSTRACT Epilepsy is a neurological disorder that affects over 50 million individuals worldwide. Today, the gold-standard treatment for those who are drug resistant, meaning that symptoms cannot be controlled with medication, is to surgically remove the seizure onset zone (SOZ), the area of the brain believed to cause seizures: the main symptom of epilepsy. Unfortunately, around 50% of drug resistant patients are not resective candidates, which can be attributed in part to poor SOZ localization. SOZ localization is a complex and lengthy procedure, requiring visual inspection and manual processing by human experts that first need to localize and isolate seizure events. The intracranial electroencephalography (iEEG) is a tool that records electrophysiological activity of the inner brain at different regions and depths, and provides critical information on the SOZ. However, iEEG data processing methodologies are not standardized, and practice and resources vary across hospitals and clinics. To assist human experts with systematic processing of iEEG data, we propose a data processing pipeline that generates graph representations of iEEG data. We evaluate 9 different graph representations of publicly available iEEG data from 25 patients with epilepsy with a graph neural network model trained to detect seizures. Our results suggest that graph representations of iEEG data that leverage electrode and functional connectivity features are powerful data structures to analyze and interpret iEEG data in the context of epilepsy. We anticipate that our data pipeline that provides a systematic processing of neural data with graphs can integrate other data modalities like neuroimaging data. Moreover, methods used in the data pipeline have potentials to apply to other neurological disorders such as Parkinson’s disease or major depression disorder.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".