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Validation of manual and automated wheezing detection from audio recordings

Samaneh Sarraf, Ronald S. Platt, Kevin Chan, Neil Skjodt
European Respiratory Journal 2021 58: PA3221; DOI: 10.1183/13993003.congress-2021.PA3221
Samaneh Sarraf
1Respiri Limited, Melbourne [Vic], Australia
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  • For correspondence: samaneh@respiri.co
Ronald S. Platt
2Sagatech Electronics Inc., Calgary [AB], Canada
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Kevin Chan
3SIOS, Leichhardt [NSW], Australia
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Neil Skjodt
4Neuroscience, University of Lethbridge, Lethbridge [AB], Canada
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Abstract

Introduction: Presently human experts must diagnose wheezing at the bedside using real-time analog tools (ear, stethoscope). Human experts could diagnose wheezing from digital audio files. Further, automated wheezing detection from unattended digital recordings would obviate bedside human diagnosis.

Aim: To validate the detection of wheezing from unattended digital recordings.

Methods: 189 digital audio recordings of 30 s duration were obtained using a dedicated digital microphone device (Wheezo, Respiri Limited, Melbourne) from 56 hospitalized patients (38 F; age 21 to 87 - mean 63.5 years; 26 COPD, 27 asthma, 1 vocal cord dysfunction, and 2 other) and 20 ambulatory normal adult controls. Wheezes were scored manually by two respirologists (KP, NS) and by a biomedical engineer (RSP). Their consensus was compared to an automated wheeze scoring algorithm. The accuracy, sensitivity, and specificity of automated wheeze rate detection were calculated along with Cohen's κ coefficient (R 3.4.4). We further plan to apply the algorithm to the ERS reference database of lung sounds.

Results: The accuracy, sensitivity, and specificity of automated wheeze rate detection were 90.5, 87.1, and 93.3%. Cohen's κ coefficient was 0.81. Breath-by-breath comparison of human and automated scoring showed near-perfect agreement for the presence, absence, and duration of wheezes.

Conclusion: Wheezing can be diagnosed from digital audio recordings either manually by human experts or automatically by a computer algorithm. Diagnosing wheezing remotely increases assessment capacity while reducing viral transmission risk during epidemics.

  • Diagnosis
  • Asthma - diagnosis
  • Wheezing

Footnotes

Cite this article as: European Respiratory Journal 2021; 58: Suppl. 65, PA3221.

This abstract was presented at the 2021 ERS International Congress, in session “Prediction of exacerbations in patients with COPD”.

This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).

  • Copyright ©the authors 2021
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Validation of manual and automated wheezing detection from audio recordings
Samaneh Sarraf, Ronald S. Platt, Kevin Chan, Neil Skjodt
European Respiratory Journal Sep 2021, 58 (suppl 65) PA3221; DOI: 10.1183/13993003.congress-2021.PA3221

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Validation of manual and automated wheezing detection from audio recordings
Samaneh Sarraf, Ronald S. Platt, Kevin Chan, Neil Skjodt
European Respiratory Journal Sep 2021, 58 (suppl 65) PA3221; DOI: 10.1183/13993003.congress-2021.PA3221
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