Automated Sleep Detection in Movement Disorders Using Deep Brain Stimulation and Machine Learning
Bibliographic record
Abstract
BACKGROUND: Automated sleep detection in movement disorders may allow monitoring sleep, potentially guiding adaptive deep brain stimulation (DBS). OBJECTIVES: The aims were to compare wake-versus-sleep status (WSS) local field potentials (LFP) in a home environment and develop biomarkers of WSS in Parkinson's disease (PD), essential tremor (ET), and Tourette's syndrome (TS) patients. METHODS: Five PD, 2 ET, and 1 TS patient were implanted with Medtronic Percept (3 STN [subthalamic nucleus], 3 GPi [globus pallidus interna], and 2 ventral intermediate nucleus). Over five to seven nights, β-band (12.5-30 Hz) and/or α-band (7-12 Hz) LFP power spectral densities were recorded. Wearable actigraphs tracked sleep. RESULTS: From sleep to wake, PD LFP β-power increased in STN and decreased in GPi, and α-power increased in both. Machine learning classifiers were trained. For PD, the highest WSS accuracy was 93% (F1 = 0.93), 86% across all patients (F1 = 0.86). The maximum accuracy was 86% for ET and 89% for TS. CONCLUSION: Chronic intracranial narrowband recordings can accurately identify sleep in various movement disorders and targets in this proof-of-concept study. © 2024 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".