Electroencephalography: electrode arrays in dogs
Bibliographic record
Abstract
Electroencephalography (EEG) is the gold standard for confirming epileptic seizures in both human and veterinary patients. Despite idiopathic epilepsy being one of the most common neurological conditions in dogs, our understanding of it in veterinary medicine lags that in human medicine. The relative underuse of EEG in dogs with seizures has potential causes including practical issues, financial concerns, lack of training/equipment, and questions of clinical value. This technological underuse may lead to, or result from, major gaps in our understanding of EEG in veterinary patients. This underutilization of EEG is of significant clinical relevance because the diagnosis of specific epilepsy syndromes in humans guides the treatment, namely pharmacological, dietary, or surgical. These epilepsy syndromes are diagnosed based on several factors, one of which is the characteristic electrical brain activity on EEG. The aim of this narrative literature review was to highlight the study of cortical brain activity to improve our understanding of EEG in veterinary medicine. Specifically, the utility of EEG with focus on the existing proposed electrode arrays and their current supporting evidence. A recent survey study confirmed that a variety of canine EEG protocols are concurrently in use, including diverse electrode arrays. By comparison, in humans there is a standardized 10-20 electrode array, with average localization error of 13-17 mm depending on the number of placed electrodes on the scalp. We offer a review of the factors that would contribute to the ideal canine EEG electrode array highlighting areas for improvement and future validation. This proposed level of understanding will facilitate the identification of cortical seizure foci with a known degree of error, paving the way for non-pharmaceutical interventions like epilepsy surgeries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".