Automated Quantification of Axial EMG Tone in Sleep Identifies Patients with REM Sleep Behavior Disorder (P05.005)
Bibliographic record
Abstract
Objective: To develop an automated tool that can quantify EMG tone in REM sleep and use it to distinguish between controls and patients with REM sleep behavior disorder (RBD). Background Schenck and Mahowald demonstrated that motor behavior in REM can predict a patient9s likelihood of developing overt Parkinson9s disease years ahead of clinical diagnosis. Motor tone in sleep can therefore provide information that may help identify patients that could be candidates for neuroprotective drug interventions. Few studies have undertaken to quantify sleep tone with an automated approach, which would facilitate screening for neurodegenerative disease. Design/Methods: A software tool to automatically quantify axial EMG tone during sleep was developed in MATLAB (MathWorks, Inc). The EMG tone of was low-pass rectified and normalized to baseline. Various measures derived included number of phasic events per hour, and mean amplitude. This tool was then used to compare the tone of patients with REM sleep behavior to age-matched controls. T-tests were used to compare the groups, with a significance level set at 0.05. Results: Six patients and six controls were assessed (4 men, 2 women; mean age = 74) with polysomnography. Patients with RBD had a greater number of phasic events per hour of sleep (1462 vs. 182, p Conclusions: An automated tool for quantification of REM sleep motor tone was able to pick out tone abnormalities in patients compared to controls. The technique is objective, and quick, and may be developed as a screening tool for milder tone abnormalities (REM sleep without atonia). The tool might therefore be able to identify patients that may be amenable to neuroprotective interventions. Supported by: Partly supported by the Comprehensive Research Experience for Medical Students (CREMS) Summer Program at the University of Toronto, Canada. Disclosure: Dr. Rasquinha has nothing to disclose. Dr. Moszczynski has nothing to disclose. Dr. Murray has received personal compensation for activities with Pfizer and Valeant as a consultant.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.003 | 0.001 |
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".