Ictal-Related Chirp as a Biomarker for Monitoring Seizure Progression
Bibliographic record
Abstract
Abstract Despite being prevalent, the causes, mechanisms, and progression of epilepsy—a chronic neurological disorder with unprovoked seizures—are not well understood, complicating drug development for treatment. This study used a comprehensive mouse epilepsy kindling model dataset to investigate frequency modulation (chirp) as a potential indicator of distinct states of epilepsy (early evoked discharge, late evoked discharge, spontaneous recurrent seizure, and drug state). Employing time-frequency ridge extraction, chirp identification, and statistical testing, our analyses revealed that chirp patterns occur in the majority of ictal discharges (>81.6%), persisting across evoked and spontaneous seizures. While the focus was on hippocampal recordings, chirps were also detected in the piriform peripheral cortex. Significant frequency and duration changes in chirp patterns during the transition from early to late evoked ictal events suggest their potential as the screening tool for seizure progression. Additionally, detailed analyses illuminate the impact of Lorazepam, a GABA A enhancer, on chirp characteristics, providing insights into how increased inhibitory tone quantifiably influences excitatory-inhibitory balances during seizures.
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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) | 0.000 | 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 | 0.000 |
| 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".