Detection of Bilateral Tonic-Clonic Seizures Using Miniaturized Wearable Electromyography-Accelerometry Sensors
Bibliographic record
Abstract
OBJECTIVE: This study aimed to develop a seizure detection algorithm using surface electromyography (sEMG) and accelerometry (ACC) signals recorded with miniaturized wearable sensors. METHODS: Continuous sEMG-ACC signals were acquired from patients wearing eight sensors positioned bilaterally on the upper trapezius, anterior deltoid, biceps brachii, and tibialis anterior muscles. We trained an extreme gradient boosting classifier to identify seizure epochs using setups with eight, two, and one sensor(s). Performance was evaluated via patient-wise nested cross-validation, and specificity was further assessed on an independent patient cohort without seizures. RESULTS: Eleven generalized tonic-clonic seizures (GTCS) and focal to bilateral tonic-clonic seizures (FBTCS) were recorded from nine patients over 1359.6 h. The best results were obtained with a dual-sensor setup combining data from the right biceps brachii and the left tibialis anterior, achieving 100% sensitivity, 0.12 FAR/24 h, and median detection latency of 22 s. On 1744.18 h of data from 19 patients without seizures, FAR/24 h was 0.06. CONCLUSION: The developed algorithm effectively detected GTCS and FBTCS in an epilepsy monitoring unit, even with a reduced number of sensors. SIGNIFICANCE: This approach could enable timely interventions in outpatient settings, potentially improving safety and independence for people with epilepsy.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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) | 0.001 | 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".