Evidence map›Paper›PMID 39821673›Full record

Observational studySleep2025

Validation of a fingertip home sleep apnea testing system using deep learning AI and a temporal event localization analysis.

Ke-Wei Chen, Chun-Hsien Tseng, Hsin-Chien Lee, Wen-Te Liu, Kun-Ta Chou, Hau-Tieng Wu

2 registry-linked trialsAbstract readObservational StudyValidation Study
PubMed Publisher
In one paragraph

Observational study in Sleep, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 2 registered trials, which are not on this map. Cited by 5 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 2 pooled it
–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.

NCT06474351 completednot on this map

Development and Fine-tuning of a Medical-grade Wearable Device with Interpretable Artificial Intelligence System for the Obstructive Sleep Apnea-hypopnea Syndrome (OSAHS) Screening

TypeobservationalSponsorPranaQ Pte. Ltd.Ran2023 to 2023Enrolled240ConditionsObstructive Sleep Apnea
NCT06633887 completednot on this map

Feasibility Evaluation of Sleep Apnea Screening with Photoplethysmography

TypeobservationalSponsorPranaQ Pte. Ltd.Ran2023 to 2024Enrolled112ConditionsObstructive Sleep Apnea
3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 2 syntheses or guidelines pooled it.

  1. Pooled it
  2. Pooled it
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Ke-Wei ChenPranaQ Pte. Ltd., Singapore.
Chun-Hsien TsengDepartment of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
Hsin-Chien LeeGraduate Institute of Humanities in Medicine, College of Humanities & Social Sciences, Taipei Medical University, Taipei, Taiwan.ORCID 0000-0002-7557-8259
Wen-Te LiuSleep Center, Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan.
Kun-Ta ChouDepartment of Chest Medicine, Taipei Veterans General Hospital, Taipei, Taiwan.
Hau-Tieng WuDepartment of Mathematics, Courant Institute of Mathematical Sciences, New York University, New York, NY, USA.ORCID 0000-0002-0253-3156

Funding

PranaQ Pte. Ltd.
6 · The paper itself

Abstract

STUDY

objectivesThis paper validates TipTraQ, a compact home sleep apnea testing (HSAT) system. TipTraQ comprises a fingertip-worn device, a mobile application, and a cloud-based deep learning artificial intelligence (AI) system. The device utilizes photoplethysmography (red, infrared, and green channels) and accelerometer sensors to assess sleep apnea by the AI system.

methodsWe prospectively enrolled 240 participants suspected of obstructive sleep apnea (OSA) at a tertiary medical center for internal validation and 112 participants independently at another center for external validation. All participants underwent simultaneous polysomnography and TipTraQ HSAT. We compared TipTraQ-derived total sleep time (TQ-TST) and TipTraQ-derived Respiratory Events Index (TQ-REI) with expert-determined TST and apnea-hypopnea index (AHI), based on AASM standards with the 1B hypopnea rule. Temporal event localization analysis for respiratory event prediction was conducted at both event and hourly levels.

resultsIn the external validation, the Spearman correlation coefficients for TQ-TST vs. TST and TQ-REI vs. AHI were 0.81 and 0.95, respectively. The root mean square error were 0.53 hours for TQ-TST vs. TST and 7.53 events/h for TQ-REI vs. AHI. For apnea/hypopnea prediction with a 10-second grace period, the true positive, false positive, and false negative rates in temporal event localization analysis were 0.76, 0.24, and 0.23, respectively. The four-way OSA severity classification achieved a Cohen's kappa of 0.7.

conclusionsTQ-TST and TQ-REI predict TST and AHI with comparable performance to existing devices of the same type, and respiratory event prediction is validated through temporal event localization analysis. CLINICAL TRIALS: Development of TipTraQ Home Sleep Test, https://clinicaltrials.gov/study/NCT06474351?term=NCT06474351&rank=1, NCT06474351TipTraQ Home Sleep Test Study, SHH, https://clinicaltrials.gov/study/NCT06633887?term=NCT06633887&rank=1, NCT06633887.

Indexed as

Deep LearningPolysomnographySleep Apnea, ObstructiveSleep Apnea SyndromesAccelerometryAdultFemaleHumansMaleMiddle AgedMobile ApplicationsPhotoplethysmographyProspective StudiesReproducibility of Resultsaccelerometerdeep learning AI systemhome sleep apnea testingphotoplethysmogram

Identifiers

PMID39821673

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

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.