A clinical evaluation of a novel algorithm in the reliable detection of epileptic seizures
Bibliographic record
Abstract
PURPOSE: Undetected and prolonged epileptic seizures can result in hypoxic brain damage or death and occur most often when the victim is in bed alone or unsupervised. Sudden unexpected death in epilepsy may not always be preventable but it is believed that timely assistance with rescue medication and body re-positioning may overcome respiratory compromise in some cases. A novel algorithm based on a real time moving 9 s epoch, calculating 25 % percentage heart rate change and/or an oxygen saturation trigger level of <85 % was developed using photoplethysmography and incorporated into a prototype data storage device. METHODS: The algorithm was clinically evaluated in this multicentre trial in the detection of clinically significant epileptic seizures. A range of epileptic seizures and normal physiological events were recorded and classified by reference standard EEG Videotelemetry and time-synchronised event data recorded by the prototype device incorporating the pre-specified cut-off points prospectively and retrospective analysis of all events. RESULTS: 119 participants who were attending electroencephalographic (EEG) videotelemetry as part of their clinical management of their epilepsy consented to take part in the trial. 683 epileptic seizures (77 clinically significant seizures) and 2648 normal physiological events were captured. When using pre-specified cut-off point 25 % heart rate change and/or oxygen desaturation <85 % on the basis of one/other, the device showed a sensitivity of 87 % for detecting clinically significant seizures. False Alarm Rate 4.5 (24 h FAR), detection latency of 58 s using heart rate percentage change. CONCLUSIONS: The results indicate that the novel algorithm can be used in detecting clinically significant seizures.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".