Heart Rate Changes and ECG Abnormalities During Epileptic Seizures: Prevalence and Definition of an Objective Clinical Sign
Bibliographic record
Abstract
PURPOSE: To determine the prevalence of heart rate changes and ECG abnormalities during epileptic seizures and to determine the timing of heart rate changes compared to the first electrographic and clinical signs. To assess the risk factors for the occurrence of ECG abnormalities. METHODS: We analyzed retrospectively 281 seizures in 81 patients with intractable epilepsy who had prolonged video-EEG and two-channel ECG. The nature and timing of heart rate changes compared to the electrographic and clinical seizure onset was determined. The ictal period (including one minute preictally and three minutes postictally) was analyzed for cardiac arrhythmias, conduction and repolarization abnormalities. Risk factors for cardiac abnormalities were investigated using parametric and non-parametric statistics. RESULTS: There was an increase in heart rate of at least 10 beats/minute in 73% of seizures (93% of patients) and this occurred most often around seizure onset. In 23% of seizures (49% of patients) the rate increase preceded both the electrographic and the clinical onset. ECG abnormalities were found in 26% of seizures (44% of patients). One patient had an asystole for 30 seconds. Long seizure duration increased the occurrence of ECG abnormalities. No other risk factor was found. CONCLUSIONS: Heart rate changes occur frequently and occur around the time or even before the earliest electrographic or clinical change. The change can clarify the timing of seizure onset and the specific rate pattern may be useful for seizure diagnosis and for automatic seizure detection. ECG abnormalities occur often and repeatedly in several seizures of the same patient.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".