Heart rate variability measurement in epilepsy: How can we move from research to clinical practice?
Bibliographic record
Abstract
Our objective was to critically evaluate the literature surrounding heart rate variability (HRV) in people with epilepsy and to make recommendations as to how future research could be directed to facilitate and accelerate integration into clinical practice. We reviewed relevant HRV publications including those involving human subjects with seizures. HRV has been studied in patients with epilepsy for more than 30 years and, overall, patients with epilepsy display altered interictal HRV, suggesting a shift in autonomic balance toward sympathetic dominance. This derangement appears more severe in those with temporal lobe epilepsy and drug-resistant epilepsy. Normal diurnal variation in HRV is also disturbed in at least some people with epilepsy, but this aspect has received less study. Some therapeutic interventions, including vagus nerve stimulation and antiepileptic medications, may partially normalize altered HRV, but studies in this area are sometimes contradictory. During seizures, the changes in HRV may be complex, but the general trend is toward a further increase in sympathetic overactivity. Research in HRV in people with epilepsy has been limited by inconsistent experimental protocols and studies that are often underpowered. HRV measurement has the potential to aid clinical epilepsy management in several possible ways. HRV may be useful in predicting which patients are likely to benefit from surgical interventions such as vagus nerve stimulation and focal cerebral resection. As well, HRV could eventually have utility as a biomarker of risk for sudden unexpected death in epilepsy (SUDEP). However, at present, the inconsistent measurement protocols used in research are hindering translation into clinical practice. A minimum protocol for HRV evaluation, to be used in all studies involving epilepsy patients, is necessary to eventually allow HRV to become a useful tool for clinicians. We propose a straightforward protocol, involving 5-minute measurements of root mean square of successive differences in wakefulness and light sleep.
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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 | 0.190 | 0.395 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.002 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.012 | 0.021 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".