Evidence map›Paper›PMID 23413133›Full record

ArticleJournal of clinical monitoring and computing2013

A system for portable sleep apnea diagnosis using an embedded data capturing module.

Hisham Alshaer, Alexander Levchenko, T Douglas Bradley, Steven Pong, Wen-Hou Tseng, Geoff R Fernie

Abstract readEvaluation Study
PubMed Publisher
In one paragraph

Article in Journal of clinical monitoring and computing, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
12citing 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.

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

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

  1. Pooled it
  2. Acoustic Analysis of Snoring in the Diagnosis of Obstructive Sleep Apnea Syndrome: A Call for More Rigorous Studies.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2015
    Pooled it
  3. Review
  4. Review
  5. Review
  6. Long-term effects of cardiac rehabilitation on sleep apnea severity in patients with coronary artery disease.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2020
    Article
  7. Objective Relationship Between Sleep Apnea and Frequency of Snoring Assessed by Machine Learning.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2019
    Article
  8. Article
  9. Article
  10. Breathing and Snoring Sound Characteristics during Sleep in Adults.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2016
    Article
  11. Article
  12. 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

6 authors.

Hisham AlshaerSleep and Upper Airway Team & Technology Team, Toronto Rehabilitation Institute, University Health Network, 550 University Avenue, Toronto, ON M5G 2A2, Canada. hisham.alshaer@uhn.ca
Alexander Levchenko
T Douglas Bradley
Steven Pong
Wen-Hou Tseng
Geoff R Fernie

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

unlabelledSleep apnea (SA) is a very common disease with serious health consequences, yet is very under-diagnosed, partially because of the high cost and limited accessibility of in-laboratory polysomnography (PSG). The purpose of this work is to introduce a newly developed portable system for the diagnosis of SA at home that is both reliable and easy to use. The system includes personal devices for recording breath sounds and airflow during sleep and diagnostic algorithms to process the recorded data. The data capturing device consists of a wearable face frame with an embedded electronic module featuring a unidirectional microphone, a differential microphone preamplifier, a microcontroller with an onboard differential analogue to digital converter, and a microSD memory card. The device provides continuous data capturing for 8 h. Upon completion of the recording session, the memory card is returned to a location for acoustic analysis. We recruited 49 subjects who used the device independently at home, after which each subject answered a usability questionnaire. Random data samples were selected to measure the signal-to-noise ratio (SNR) as a gauge of hardware functionality. A subset of 11 subjects used the device on 2 different nights and their results were compared to examine diagnostic reproducibility. Independent of those, system's performance was evaluated against PSG in the lab environment in 32 subject. The overall success rate of applying the device in un-attended settings was 94 % and the overall rating for ease-of-use was 'excellent'. Signal examination showed excellent capturing of breath sounds with an average SNR of 31.7 dB. Nine of the 11 (82 %) subjects had equivalent results on both nights, which is consistent with reported inter-night variability. The system showed 96 % correlation with simultaneously performed in-lab PSG.

conclusionOur results suggest excellent usability and performance of this system and provide a strong rationale to further improve it and test its robustness in a larger study.

Indexed as

AcousticsAlgorithmsDiagnosis, Computer-AssistedEquipment DesignHumansMonitoring, AmbulatoryPolysomnographyReproducibility of ResultsRespiratory SoundsSignal-To-Noise RatioSleep Apnea Syndromes

Identifiers

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.