Actigraphic detection of periodic limb movements: development and validation of a potential device-independent algorithm. A proof of concept study
Bibliographic record
Abstract
STUDY OBJECTIVES: We propose a unique device-independent approach to analyze long-term actigraphy signals that can accurately quantify the severity of periodic limb movements in sleep (PLMS). METHODS: We analyzed 6-8 hr of bilateral ankle actigraphy data for 166 consecutively consenting patients who simultaneously underwent routine clinical polysomnography. Using the proposed algorithm, we extracted 14 time and frequency features to identify PLMS. These features were then used to train a Naïve-Bayes learning tool which permitted classification of mild vs. severe PLMS (i.e. periodic limb movements [PLM] index less than vs. greater than 15 per hr), as well as classification for four PLM severities (i.e. PLM index < 15, between 15 and 29.9, between 30 and 49.9, and ≥50 movements per hour). RESULTS: Using the proposed signal analysis technique, coupled with a leave-one-out cross-validation method, we obtained a classification accuracy of 89.6%, a sensitivity of 87.9%, and a specificity of 94.1% when classifying a PLM index less than vs. greater than 15 per hr. For the multiclass classification for the four PLM severities, we obtained a classification accuracy of 85.8%, with a sensitivity of 97.6%, and a specificity of 84.8%. CONCLUSIONS: Our approach to analyzing long-term actigraphy data provides a method that can be used as a screening tool to detect PLMS using actigraphy devices from various manufacturers and will facilitate detection of PLMS in an ambulatory setting.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".