Evidence map›Paper›PMID 37231138›Full record

ArticleScientific reports2023

Cough sound-based estimation of vital capacity via cough peak flow using artificial neural network analysis.

Yasutaka Umayahara, Zu Soh, Akira Furui, Kiyokazu Sekikawa, Takeshi Imura, Akira Otsuka, Toshio Tsuji

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Yasutaka UmayaharaGraduate School of Health Sciences, Hiroshima Cosmopolitan University, 3-2-1 Otsukahigashi, Asaminami-ku, Hiroshima, Japan. umayahara-creha@umin.ac.jp.
Zu SohGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan.
Akira FuruiGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan.
Kiyokazu SekikawaGraduate School of Biomedical and Health Sciences, Hiroshima University, 1-2-3 Kasumi, Minami-ku, Hiroshima, Japan.
Takeshi ImuraGraduate School of Health Sciences, Hiroshima Cosmopolitan University, 3-2-1 Otsukahigashi, Asaminami-ku, Hiroshima, Japan.
Akira OtsukaGraduate School of Health Sciences, Hiroshima Cosmopolitan University, 3-2-1 Otsukahigashi, Asaminami-ku, Hiroshima, Japan.
Toshio TsujiGraduate School of Advanced Science and Engineering, Hiroshima University, 1-4-1 Kagamiyama, Higashi-Hiroshima, Hiroshima, Japan. tsuji-c@bsys.hiroshima-u.ac.jp.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study presents a novel approach for estimating vital capacity using cough sounds and proposes a neural network-based model that utilizes the reference vital capacity computed using the lambda-mu-sigma method, a conventional approach, and the cough peak flow computed based on the cough sound pressure level as inputs. Additionally, a simplified cough sound input model is developed, with the cough sound pressure level used directly as the input instead of the computed cough peak flow. A total of 56 samples of cough sounds and vital capacities were collected from 31 young and 25 elderly participants. Model performance was evaluated using squared errors, and statistical tests including the Friedman and Holm tests were conducted to compare the squared errors of the different models. The proposed model achieved a significantly smaller squared error (0.052 L

Indexed as

CoughSoundAgedHumansNeural Networks, ComputerPeak Expiratory Flow RateVital Capacity

Identifiers

PMID37231138
PMCPMC10212959

What OpenQuestion holds

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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.