Literature DB >> 1628726

Towards a quantitative description of asthmatic cough sounds.

C W Thorpe1, L J Toop, K P Dawson.   

Abstract

This study describes a method of quantitatively characterizing cough sounds using digital signal processing techniques. Differences between asthmatic and non-asthmatic cough sounds are presented. Coughs from 12 asthmatic and 5 non-asthmatic subjects were analysed. Cough sounds and flows were digitized, at a sampling rate of 5 kHz, before and after a free-running exercise test. Individual coughs were divided into two or three phases, corresponding to the initial glottal opening burst, the quieter middle phase, and (sometimes) the final closing burst. Standard signal processing techniques were then invoked to characterize the spectral and temporal shapes of the first two phases. Factor analysis indicated that the spectral shapes of the two phases are independent, with each being largely described by the degree of "peakedness" in the spectrum, and by the balance of energy between low and high frequencies. Both the duration of the initial burst and zero-crossing rates of the cough waveform (which indicates the "spectral balance") during each of the first two phases were smaller for asthmatic than for non-asthmatic coughs. Fewer asthmatic coughs contained a final burst. Discriminant analysis between the two groups gave classification error rates of 20-30%. The peak flow recorded during the cough was significantly smaller for asthmatics, and correlated very well with the peak flow recorded during forced expiration. Thus, significant differences exist between asthmatic and non-asthmatic cough sounds. An effective representation of the temporal structure of the cough sound is required to successfully characterize the cough.

Mesh:

Year:  1992        PMID: 1628726

Source DB:  PubMed          Journal:  Eur Respir J        ISSN: 0903-1936            Impact factor:   16.671


  8 in total

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Journal:  Br J Clin Pharmacol       Date:  2001-10       Impact factor: 4.335

2.  The description of cough sounds by healthcare professionals.

Authors:  Jaclyn A Smith; H Louise Ashurst; Sandy Jack; Ashley A Woodcock; John E Earis
Journal:  Cough       Date:  2006-01-25

3.  Acoustery System for Differential Diagnosing of Coronavirus COVID-19 Disease.

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Journal:  IEEE Open J Eng Med Biol       Date:  2021-11-10

4.  An advanced recording and analysis system for the differentiation of guinea pig cough responses to citric acid and prostaglandin E2 in real time.

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Journal:  PLoS One       Date:  2019-05-22       Impact factor: 3.240

Review 5.  Past and Trends in Cough Sound Acquisition, Automatic Detection and Automatic Classification: A Comparative Review.

Authors:  Antoine Serrurier; Christiane Neuschaefer-Rube; Rainer Röhrig
Journal:  Sensors (Basel)       Date:  2022-04-10       Impact factor: 3.847

6.  Classification of voluntary cough sound and airflow patterns for detecting abnormal pulmonary function.

Authors:  Ayman A Abaza; Jeremy B Day; Jeffrey S Reynolds; Ahmed M Mahmoud; W Travis Goldsmith; Walter G McKinney; E Lee Petsonk; David G Frazer
Journal:  Cough       Date:  2009-11-20

Review 7.  Global Physiology and Pathophysiology of Cough: Part 1: Cough Phenomenology - CHEST Guideline and Expert Panel Report.

Authors:  Kai K Lee; Paul W Davenport; Jaclyn A Smith; Richard S Irwin; Lorcan McGarvey; Stuart B Mazzone; Surinder S Birring
Journal:  Chest       Date:  2020-09-02       Impact factor: 9.410

8.  Sound: a non-invasive measure of cough intensity.

Authors:  Kai K Lee; Sergio Matos; Katie Ward; Gerrard F Rafferty; John Moxham; David H Evans; Surinder S Birring
Journal:  BMJ Open Respir Res       Date:  2017-05-12
  8 in total

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