A Comparison of Two Deep Learning Approaches to Distinguish Functional Dissociative from Epileptic Seizures Using Event Videos
Bibliographic record
Abstract
BACKGROUND: Differentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (ES) is a common diagnostic challenge, requiring video electroencephalography (vEEG) as gold standard. However, vEEG requires specialized technicians and clinical experts to set up and interpret and oftentimes fails to capture events. We sought to develop machine-learning (ML) tools to carry out this diagnostic task independently of vEEG or human review by a neurologist. METHODS: In this retrospective study, we developed two proof-of-concept ML models to differentiate motor ES from FDS based on video of FDS and ES events in patients who underwent inpatient vEEG monitoring at an academic medical center between 2012 and 2021. The first model employed a pose-estimation approach, using body landmark features that were labeled frame-by-frame by three neurologists. The second utilized an end-to-end 3D convolutional neural network (CNN), thereby learning directly from raw video frames. Using board-certified epileptologist review as a clinical gold standard, we measured model performance by area under the receiver-operating (AUROC) and precision-recall (AUPRC) curves, sensitivity, precision, and accuracy against a held-out test set of videos. RESULTS: We included 101 unique patients with 106 total event videos, comprising 61 (60.4%) ES and 45 (44.6%) FDS events. Both ML models distinguished both seizure types better than chance. The pose-estimation-based model achieved an AUROC of 0.71, AUPRC 0.53, sensitivity 0.90, precision 0.50, and accuracy 0.62. The CNN model exhibited superior overall performance, achieving an AUROC 0.78, AUPRC 0.84, balanced sensitivity and precision (both 0.82), and accuracy 0.80. CONCLUSION: Our findings demonstrate the superiority of CNN over pose-estimation models to differentiate between motor ES and FDS using video alone. Although future studies are needed, these models hold potential as adjunct diagnostic tools by enabling rapid, objective seizure evaluations without immediate neurologist involvement.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".