Evidence map›Paper›PMID 36818388›Full record

ArticleJournal of healthcare engineering2023

Monitoring of Sleep Breathing States Based on Audio Sensor Utilizing Mel-Scale Features in Home Healthcare.

Yu Fang, Dongbo Liu, Zhongwei Jiang, Haibin Wang

Abstract read
PubMed Full text at PMC Publisher
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Article in Journal of healthcare engineering, 2023. 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
–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

2 citing papers in PubMed.

  1. Review
  2. 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

4 authors.

Yu FangSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610000, China.ORCID 0000-0002-0209-2071
Dongbo LiuSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610000, China.ORCID 0000-0002-6743-0959
Zhongwei JiangGraduate School of Science and Technology for Innovation, Yamaguchi University, Ube 755-0096, Japan.ORCID 0000-0003-3772-4814
Haibin WangSchool of Electrical Engineering and Electronic Information, Xihua University, Chengdu 610000, China.ORCID 0000-0002-3053-3890

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Sleep-related breathing disorders (SBDs) will lead to poor sleep quality and increase the risk of cardiovascular and cerebrovascular diseases which may cause death in serious cases. This paper aims to detect breathing states related to SBDs by breathing sound signals. A moment waveform analysis is applied to locate and segment the breathing cycles. As the core of our study, a set of useful features of breathing signal is proposed based on Mel frequency cepstrum analysis. Finally, the normal and abnormal sleep breathing states can be distinguished by the extracted Mel-scale indexes. Young healthy testers and patients who suffered from obstructive sleep apnea are tested utilizing the proposed method. The average accuracy for detecting abnormal breathing states can reach 93.1%. It will be helpful to prevent SBDs and improve the sleep quality of home healthcare.

Indexed as

RespirationSleep Apnea, ObstructiveDelivery of Health CareHumansPolysomnographySleep

Identifiers

PMID36818388
PMCPMC9935909

What OpenQuestion holds

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LicenceCC BY
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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.