Evidence map›Paper›PMID 33302407›Full record

ArticleSensors (Basel, Switzerland)2020

A New Wearable System for Home Sleep Apnea Testing, Screening, and Classification.

Alessandro Manoni, Federico Loreti, Valeria Radicioni, Daniela Pellegrino, Luigi Della Torre, Alessandro Gumiero, Damian Halicki, Paolo Palange, Fernanda Irrera

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 3 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
28citing papers in PubMed, 3 pooled it
4.0field-weighted citation impact, top 5% of its field
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

28 citing papers in PubMed, 3 syntheses or guidelines pooled it, 69 citations in OpenAlex.

  1. Pooled it
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  14. Observational
  15. Multimodal Sleep Apnea Detection with Missing or Noisy Modalities.Proceedings of machine learning research · 2024
    Article
  16. Article
  17. Review
  18. Review
  19. Article
  20. Digital health in chronic obstructive pulmonary disease.Chronic diseases and translational medicine · 2023
    Review
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

9 authors at 2 institutions in 1 country.

Alessandro ManoniDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy.ORCID 0000-0003-1232-0354
Federico LoretiDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy.
Valeria RadicioniSTMicroelectronics, Agrate Brianza, 20864 MB, Italy.
Daniela PellegrinoDepartment of Public Health and Infectious Diseases, Sapienza University of Rome, 00185 Rome, Italy.
Luigi Della TorreSTMicroelectronics, Agrate Brianza, 20864 MB, Italy.
Alessandro GumieroSTMicroelectronics, Agrate Brianza, 20864 MB, Italy.
Damian HalickiSTMicroelectronics, Agrate Brianza, 20864 MB, Italy.
Paolo PalangeDepartment of Public Health and Infectious Diseases, Sapienza University of Rome, 00185 Rome, Italy.
Fernanda IrreraDepartment of Information Engineering, Electronics and Telecommunications, Sapienza University of Rome, 00184 Rome, Italy.ORCID 0000-0003-1831-6786
Sapienza University of Rome · ITSTMicroelectronics (Italy) · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We propose an unobtrusive, wearable, and wireless system for the pre-screening and follow-up in the domestic environment of specific sleep-related breathing disorders. This group of diseases manifests with episodes of apnea and hypopnea of central or obstructive origin, and it can be disabling, with several drawbacks that interfere in the daily patient life. The gold standard for their diagnosis and grading is polysomnography, which is a time-consuming, scarcely available test with many wired electrodes disseminated on the body, requiring hospitalization and long waiting times. It is limited by the night-by-night variability of sleep disorders, while inevitably causing sleep alteration and fragmentation itself. For these reasons, only a small percentage of patients achieve a definitive diagnosis and are followed-up. Our device integrates photoplethysmography, an accelerometer, a microcontroller, and a bluetooth transmission unit. It acquires data during the whole night and transmits to a PC for off-line processing. It is positioned on the nasal septum and detects apnea episodes using the modulation of the photoplethysmography signal during the breath. In those time intervals where the photoplethysmography is detecting an apnea, the accelerometer discriminates obstructive from central type thanks to its excellent sensitivity to thoraco-abdominal movements. Tests were performed on a hospitalized patient wearing our integrated system and the type III home sleep apnea testing recommended by The American Academy of Sleep Medicine. Results are encouraging: sensitivity and precision around 90% were achieved in detecting more than 500 apnea episodes. Least thoraco-abdominal movements and body position were successfully classified in lying down control subjects, paving the way toward apnea type classification.

Indexed as

Sleep Apnea, ObstructiveSleep Apnea SyndromesWearable Electronic DevicesHumansPolysomnographySleepaccelerometerapnea typedomestic screeningphotoplethysmographysignal fusionsleep apneaswearable devices

Identifiers

PMID33302407
PMCPMC7762585
OpenAlexW3112188401

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.