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

doi:10.1183/23120541.00053-2022

Acoustic surveillance of cough for detecting respiratory disease using artificial intelligence

ERJ Open Res. 2022 May 30;8(2):00053-2022. doi: 10.1183/23120541.00053-2022. eCollection 2022 Apr.

Authors

Juan C Gabaldón-Figueira^{1

2}, Eric Keen³, Gerard Giménez³, Virginia Orrillo⁴, Isabel Blavia⁴, Dominique Hélène Doré⁵, Nuria Armendáriz⁶, Juliane Chaccour¹, Alejandro Fernandez-Montero⁷, Javier Bartolomé⁶, Nita Umashankar⁸, Peter Small^{3

9}, Simon Grandjean Lapierre^{5

10

11}, Carlos Chaccour^{1

2

12

11}

Affiliations

¹ Dept of Microbiology and Infectious Diseases, Clinica Universidad de Navarra, Pamplona, Spain.
² ISGlobal, Hospital Clinic, University of Barcelona, Barcelona, Spain.
³ Research and Development Dept, Hyfe Inc, Wilmington, DE, USA.
⁴ School of Pharmacy and Nutrition, University of Navarra, Pamplona, Spain.
⁵ Immunopathology Axis, Research Center of the University of Montreal Hospital Center, Montréal, QC, Canada.
⁶ Primary Healthcare, Navarra Health Service-Osasunbidea, Zizur Mayor, Spain.
⁷ Dept of Occupational Medicine - COVID-19 Area, Clinica Universidad de Navarra, Pamplona, Spain.
⁸ Fowler College of Business, San Diego State University, San Diego, CA, USA.
⁹ Dept of Global Health, University of Washington, Seattle, WA, USA.
¹⁰ Dept of Microbiology, Infectious Diseases and Immunology, Research Center of the University of Montreal Hospital Center, Montreal, QC, Canada.
¹¹ These authors contributed equally.
¹² Centro de Investigación Biomédica en Red de Enfermedades Infecciosas, Madrid, Spain.

Abstract

Research question: Can smartphones be used to detect individual and population-level changes in cough frequency that correlate with the incidence of coronavirus disease 2019 (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 analysis, and its strength determined by calculating its autocorrelation function (ACF). Predictors for the regular use of the system were studied using a linear regression. Overall user experience was evaluated using a satisfaction questionnaire and through focused group discussions.

Results: We followed-up 616 participants and collected >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.