Optimal graph representations and neural networks for multichannel time series data in seizure phase classification
Bibliographic record
Abstract
In recent years, several machine-learning (ML) solutions have been proposed to solve the problems of seizure detection, seizure phase classification, seizure prediction, and seizure onset zone (SOZ) localization, achieving excellent performance with accuracy levels above 95%. However, none of these solutions has been fully deployed in clinical settings. The primary reason has been a lack of trust from clinicians towards the complex decision-making operability of ML. More recently, research efforts have focused on systematized and generalizable frameworks of ML models that are clinician-friendly. In this paper, we propose a generalizable pipeline that leverages graph representation data structures as a flexible tool for graph neural networks. Moreover, we conducted an analysis of graph neural networks (GNN), a paradigm of artificial neural networks optimized to operate on graph-structured data, as a framework to classify seizure phases (preictal vs. ictal vs. postictal) from intracranial electroencephalographic (iEEG) data. We employed two multi-center international datasets, comprising 23 and 16 patients and 5 and 7 h of iEEG recordings. We evaluated four GNN models, with the highest performance achieving a seizure phase classification accuracy of 97%, demonstrating its potential for clinical application. Moreover, we show that by leveraging t-SNE, a statistical method for visualizing high-dimensional data, we can analyze how GNN's influence the iEEG and graph representation embedding space. We also discuss the scientific implications of our findings and provide insights into future research directions for enhancing the generalizability of ML models in clinical practice.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".