Evidence map›Paper›PMID 41571940›Full record

ArticleNPJ digital medicine2026

Personalized supervised and unsupervised intracranial sleep decoding during deep brain stimulation.

Clay Smyth, Md Fahim Anjum, Jin-Xiao Zhang, Jiaang Yao, Reza Abbasi-Asl, Philip Starr, Simon Little

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Clay SmythDepartment of Bioengineering, University of California, San Francisco, San Francisco, CA, USA. clay.smyth@ucsf.edu.
Md Fahim AnjumDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Jin-Xiao ZhangDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Jiaang YaoDepartment of Bioengineering, University of California, San Francisco, San Francisco, CA, USA.
Reza Abbasi-AslDepartment of Neurology, University of California, San Francisco, San Francisco, CA, USA.
Philip Starr *Department of Neurosurgery, University of California, San Francisco, San Francisco, CA, USA.
Simon Little *Department of Neurology, University of California, San Francisco, San Francisco, CA, USA.

Funding

Determining the circuits and signals of sleep dysfunction in Parkinson's disease through chronic intracranial recordings and closed-loop Deep Brain StimulationR01NS131405 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI Simon Little · 2023 to 2026
$2.6M
Simplified, Scalable 24-hour adaptive deep brain stimulation for Parkinson’s diseaseUG3NS140730 · NINDS · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI LITTLE, SIMON, STARR, PHILIP ANDREW · 2025 to 2025
$1.4M
NIH HHS R01NS131405NIH HHS UG3NS140730NINDS NIH HHS R01 NS131405NINDS NIH HHS UG3 NS140730
6 · The paper itself

Abstract

Impaired sleep in Parkinson's Disease (PD) is a significant unmet need. Targeting sleep stage-specific neurophysiologies with adaptive Deep Brain Stimulation (aDBS) may ameliorate sleep disruption. This study analyzes the efficacy of personalized machine learning approaches on classifying sleep stages from participants receiving deep brain stimulation. We acquired 283 hours of multi-night intracranial cortico-basal recordings with synchronized sleep stage labels derived from scalp EEG across 5 participants during chronic stimulation. Five-stage classification accuracy across PD subjects averaged 80.2% (±0.9% SEM). When constraining sleep classification to algorithms implementable in currently available DBS devices, e.g., binary NREM classification using linear models, an average accuracy of 85.9% (±0.4% SEM) was achieved for PD subjects. Additionally, linear models trained on unsupervised cluster labels achieved an average accuracy of 83.5% (±5.6% SEM) when discriminating NREM sleep. Overall, this demonstrates the feasibility of personalized supervised and unsupervised ML models for sleep classification using intracranial data during stimulation. The Institutional Review Board approved the parent study protocol, and the study was registered on clinicaltrials.gov (NCT0358289; IDE G180097).

Identifiers

PMID41571940
PMCPMC13376363

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.