Evidence map›Paper›PMID 33375762›Full record

ArticleSensors (Basel, Switzerland)2020

Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration.

Xinyue Lu, Christine Azevedo Coste, Marie-Cécile Nierat, Serge Renaux, Thomas Similowski, David Guiraud

Open access · goldAbstract read
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 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
1.0field-weighted citation impact, top 19% 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

2 citing papers in PubMed, 11 citations in OpenAlex.

  1. Article
  2. 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 at 3 institutions in 1 country.

Xinyue LuFaculté des Sciences, University of Montpellier, F-34090 Montpellier, France.ORCID 0000-0002-0896-2621
Christine Azevedo CosteINRIA, F-34090 Montpellier, France.ORCID 0000-0002-7379-8004
Marie-Cécile NieratUMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, INSERM, Sorbonne Université, F-75005 Paris, France.ORCID 0000-0001-9370-7301
Serge RenauxNeuroResp, F-34600 Les Aires, France.
Thomas SimilowskiUMRS1158 Neurophysiologie Respiratoire Expérimentale et Clinique, INSERM, Sorbonne Université, F-75005 Paris, France.ORCID 0000-0003-2868-9279
David GuiraudINRIA, F-34090 Montpellier, France.ORCID 0000-0002-4184-6243
Inserm · FRInstitut national de recherche en sciences et technologies du numérique · FRUniversité de Montpellier · FR

Funding

Association Nationale de la Recherche et de la Technologie N° 2016/1600
6 · The paper itself

Abstract

Patients with central respiratory paralysis can benefit from diaphragm pacing to restore respiratory function. However, it would be important to develop a continuous respiratory monitoring method to alert on apnea occurrence, in order to improve the efficiency and safety of the pacing system. In this study, we present a preliminary validation of an acoustic apnea detection method on healthy subjects data. Thirteen healthy participants performed one session of two 2-min recordings, including a voluntary respiratory pause. The recordings were post-processed by combining temporal and frequency detection domains, and a new method was proposed-Phonocardiogram-Derived Respiration (PDR). The detection results were compared to synchronized pneumotachograph, electrocardiogram (ECG), and abdominal strap (plethysmograph) signals. The proposed method reached an apnea detection rate of 92.3%, with 99.36% specificity, 85.27% sensitivity, and 91.49% accuracy. PDR method showed a good correlation of 0.77 with ECG-Derived Respiration (EDR). The comparison of R-R intervals and S-S intervals also indicated a good correlation of 0.89. The performance of this respiratory detection algorithm meets the minimal requirements to make it usable in a real situation. Noises from the participant by speaking or from the environment had little influence on the detection result, as well as body position. The high correlation between PDR and EDR indicates the feasibility of monitoring respiration with PDR.

Indexed as

ElectrocardiographyRespirationAlgorithmsApneaHumansMonitoring, PhysiologicSignal Processing, Computer-Assistedbreathing detectionPhonocardiogram-Derived Respiration (PDR)respiratory monitoringtracheal sounds

Identifiers

PMID33375762
PMCPMC7795986
OpenAlexW3113796506

What OpenQuestion holds

Textmetadata
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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.