Evidence map›Paper›PMID 33111168›Full record

ArticleSleep & breathing = Schlaf & Atmung2021

Severity evaluation of obstructive sleep apnea based on speech features.

Yiming Ding, Jiaxi Wang, Jiandong Gao, Qiang Fang, Yanru Li, Wen Xu, Ji Wu, Demin Han

Abstract read
PubMed Publisher
In one paragraph

Article in Sleep & breathing = Schlaf & Atmung, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 2 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed, 2 pooled it
1.0field-weighted citation impact, top 24% 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

4 citing papers in PubMed, 2 syntheses or guidelines pooled it, 16 citations in OpenAlex.

  1. Pooled it
  2. Awake speech recordings for machine learning diagnosis of obstructive sleep apnea: a Bayesian meta-analysis.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2025
    Pooled it
  3. Review
  4. Selection of OSA-specific pronunciations and assessment of disease severity assisted by machine learning.Journal of clinical sleep medicine : JCSM : official publication of the American Academy of Sleep Medicine · 2022
    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

8 authors at 4 institutions in 1 country.

Yiming Ding *Beijing Tongren Hospital, Capital Medical University, 1, Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People's Republic of China.
Jiaxi Wang *Department of Electronic Engineering, Tsinghua University, Room 8301, Luomu Building, Beijing, China.
Jiandong GaoDepartment of Electronic Engineering, Tsinghua University, Room 8301, Luomu Building, Beijing, China.
Qiang FangInstitute of Linguistics, Chinese Academy of Social Sciences, Beijing, China.
Yanru LiBeijing Tongren Hospital, Capital Medical University, 1, Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People's Republic of China.
Wen XuBeijing Tongren Hospital, Capital Medical University, 1, Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People's Republic of China.
Ji WuDepartment of Electronic Engineering, Tsinghua University, Room 8301, Luomu Building, Beijing, China. wuji_ee@mail.tsinghua.edu.cn.
Demin HanBeijing Tongren Hospital, Capital Medical University, 1, Dongjiaominxiang Street, Dongcheng District, Beijing, 100730, People's Republic of China. deminhan_ent@hotmail.com.ORCID http://orcid.org/0000-0002-2180-7394
Beijing Tongren Hospital · CNTsinghua University · CNBeijing Institute of Big Data Research · CNChinese Academy of Social Sciences · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeThere are upper airway abnormalities in patients with obstructive sleep apnea (OSA), and their speech signal characteristics are different from those of unaffected people. In this study, the severity of OSA was evaluated automatically by machine learning technology based on the speech signals of Chinese people.

methodsIn total, 151 adult male Mandarin native speakers who had suspected OSA completed polysomnography to assess the severity of the disease. Chinese vowels and nasal sounds were recorded in sitting and supine positions, and the accuracy of predicting the apnea-hypopnea index (AHI) of the participants using a machine learning method was analyzed based on features extracted from the speech signals.

resultsAmong the 151 participants, 75 had AHI > 30 events/h, and 76 had AHI ≤ 30 events/h. Various features including linear prediction cepstral coefficients (LPCC) were extracted from the data collected from participants recorded in the sitting and supine positions and by using a linear support vector machine (SVM); we classified the participants with thresholds of AHI = 30 and AHI = 10 events/h. The accuracies of the classifications were both 78.8%, the sensitivities were 77.3% and 79.1%, and the specificities were 80.3% and 78.0%, respectively.

conclusionThis study constructed a severity evaluation model of OSA based on speech signal processing and machine learning, which can be used as an effective method to screen patients with OSA. In addition, it was found that Chinese pronunciation can be used as an effective feature to predict OSA.

Indexed as

AdultChinaHumansMaleMiddle AgedPatient AcuityPolysomnographySignal Processing, Computer-AssistedSleep Apnea, ObstructiveSpeechSupport Vector MachineMachine learningObstructive sleep apnea (OSA)Speech signal processing

Identifiers

PMID33111168
OpenAlexW3096647374

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.