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Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence

Juan C. Gabaldón-Figueira, Eric Keen, Gerard Giménez, Virginia Orrillo, Isabel Blavia, Dominique Hélène Doré, Nuria Armendáriz, Juliane Chaccour, Alejandro Fernandez-Montero, Javier Bartolomé, Nita Umashankar, Peter Small, Simon Grandjean Lapierre, Carlos Chaccour
ERJ Open Research 2022; DOI: 10.1183/23120541.00053-2022
Juan C. Gabaldón-Figueira
1Department of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain
2ISGlobal, Hospital Clinic, University of Barcelona, Barcelona, Spain
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  • ORCID record for Juan C. Gabaldón-Figueira
  • For correspondence: juancarlos.gabaldon@isglobal.org
Eric Keen
3Research and Development Department, Hyfe Inc, Wilmington, Delaware, USA
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Gerard Giménez
3Research and Development Department, Hyfe Inc, Wilmington, Delaware, USA
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Virginia Orrillo
4School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
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Isabel Blavia
4School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain
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Dominique Hélène Doré
5Immunopathology Axis, Research Center of the University of Montreal Hospital Center, Montréal, Québec, Canada
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Nuria Armendáriz
6Primary Healthcare, Navarra Health Service-Osasunbidea, Zizur Mayor, Spain
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Juliane Chaccour
1Department of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain
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Alejandro Fernandez-Montero
7Department of Occupational Medicine - COVID-19 area, University of Navarra Clinic, Pamplona, Spain
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Javier Bartolomé
6Primary Healthcare, Navarra Health Service-Osasunbidea, Zizur Mayor, Spain
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Nita Umashankar
8Fowler College of Business, San Diego State University, San Diego, California, USA
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Peter Small
3Research and Development Department, Hyfe Inc, Wilmington, Delaware, USA
9Department of Global Health, University of Washington, Seattle, WA, USA
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Simon Grandjean Lapierre
5Immunopathology Axis, Research Center of the University of Montreal Hospital Center, Montréal, Québec, Canada
10Department of Microbiology, Infectious Diseases and Immunology, Research Center of the University of Montreal Hospital Center, Montreal, Québec, Canada
12These authors contributed equally
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Carlos Chaccour
1Department of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain
11Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Madrid, Spain
2ISGlobal, Hospital Clinic, University of Barcelona, Barcelona, Spain
12These authors contributed equally
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Abstract

Research question Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of COVID-19 and other respiratory infections?

Methods This was a prospective cohort study carried out in Pamplona (Spain) between 2020 and 2021 using artificial intelligence cough detection software. Changes in cough frequency around the time of medical consultation were evaluated using a randomisation routine, significance was tested by comparing the distribution of cough frequencies to that obtained from a model of no difference. The correlation between changes of cough frequency and COVID-19 incidence was studied using an autoregressive moving average (ARIMA) analysis, and its strength determined by calculating its auto-correlation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated with a satisfaction questionnaire and through focused group discussions.

Results We followed up 616 participants and collected over 62 000 coughs. Coughs per hour surged around the time cohort subjects sought medical care (difference=+0.77 coughs h−1, p=0.00001) There was a weak temporal correlation between aggregated coughs and the incidence of COVID-19 in the local population (ACF=0.43). Technical issues affected uptake and regular use of the system.

Interpretation Artificial intelligence systems can detect changes in cough frequency that temporarily correlate with the onset of clinical disease at the individual level. A clearer correlation with population-level COVID-19 incidence, or other respiratory conditions, could be achieved with better penetration and compliance with cough monitoring.

Footnotes

This manuscript has recently been accepted for publication in the ERJ Open Research. It is published here in its accepted form prior to copyediting and typesetting by our production team. After these production processes are complete and the authors have approved the resulting proofs, the article will move to the latest issue of the ERJOR online. Please open or download the PDF to view this article.

Conflict of interest: none

This is a PDF-only article. Please click on the PDF link above to read it.

  • Received February 3, 2022.
  • Accepted March 23, 2022.
  • Copyright ©The authors 2022
http://creativecommons.org/licenses/by-nc/4.0/

This version is distributed under the terms of the Creative Commons Attribution Non-Commercial Licence 4.0. For commercial reproduction rights and permissions contact permissions{at}ersnet.org

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Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence
Juan C. Gabaldón-Figueira, Eric Keen, Gerard Giménez, Virginia Orrillo, Isabel Blavia, Dominique Hélène Doré, Nuria Armendáriz, Juliane Chaccour, Alejandro Fernandez-Montero, Javier Bartolomé, Nita Umashankar, Peter Small, Simon Grandjean Lapierre, Carlos Chaccour
ERJ Open Research Jan 2022, 00053-2022; DOI: 10.1183/23120541.00053-2022

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Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence
Juan C. Gabaldón-Figueira, Eric Keen, Gerard Giménez, Virginia Orrillo, Isabel Blavia, Dominique Hélène Doré, Nuria Armendáriz, Juliane Chaccour, Alejandro Fernandez-Montero, Javier Bartolomé, Nita Umashankar, Peter Small, Simon Grandjean Lapierre, Carlos Chaccour
ERJ Open Research Jan 2022, 00053-2022; DOI: 10.1183/23120541.00053-2022
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