NCT05042063

Acoustic Cough Monitoring for the Management of Patients With Known Respiratory Disease

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 616 (actual)
• Sponsor: Clinica Universidad de Navarra, Universidad de Navarra · Academic / Other
• Sex: All
• Age: 5 Years – 100 Years
• Healthy volunteers: Accepted

Summary

This study pretends to evaluate the potential use of Hyfe Cough Tracker (Hyfe) to screen for, diagnose, and support the clinical management of patients with respiratory diseases, while enriching a dataset of disease-specific annotated coughs, for further refinement of similar systems.

Detailed description

This is an observational study that will take place in the two campuses of the Clínica Universidad de Navarra, located in Pamplona and Madrid (Spain). An Artificial-Intelligence system (AI) that detects and records explosive putative cough sounds and identifies human cough based on acoustic characteristics will be used to automatically monitor cough. Potential participants either attending the outpatient clinic or hospitalised with a complaint of cough will be invited by their treating physician, or a member of the research team, and included in the study by part of the research team. A researcher will instruct participants on how to install and use Hyfe Cough Tracker in their smartphones. Participants will be monitored for 30 days (outpatients) or until discharged from the hospital (inpatients). Participants will be asked to complete a daily, online, standardised 100 mm visual analogue scale (VAS) to register changes in the subjective intensity of their cough, while using Hyfe to objectively monitor changes in its frequency. In parallel, a dataset of annotated cough sounds will be constructed and retrospectively used to assess differences in acoustic patterns of cough, and to evaluate the performance of the system detecting them. A first subgroup of participants will be recruited outside the clinical setting and asked to provide a series of elicited sounds, including coughs, which will then be used to determine the system's performance accurately discriminating coughs from non-cough sounds, and compared to trained human listeners. A second subgroup of participants will be will be instructed to use Hyfe, and the related Hyfe Air wearable device continuously for a period between 6 and 24 hours, while they record themselves using a MP3 recorder connected to a lapel microphone. This group will be used to evaluate the performance of Hyfe and Hyfe Air in a real-life setting, with spontaneous coughs.

Conditions

• Cough
• COPD
• GERD
• Asthma
• Tuberculosis
• Non-Tuberculous Mycobacterial Pneumonia
• COVID-19 Pneumonia

Interventions

Type	Name	Description
DEVICE	Hyfe Cough Tracker	Hyfe Cough Tracker is a digital acoustic surveillance system that uses an artificial intelligence system to discriminate cough from non-cough sounds. Hyfe is an AI-enabled mobile app that records short snippets (\<0.5 seconds) of putative cough explosive sounds and then classifies them as cough or non-cough using a convolutional neural network (CNN) model. Briefly, the acoustic characteristics of recorded sounds are converted into an image file, which is then processed by an algorithm trained to identify graphical differences in images. This creates an adjustable prediction score, with values above it, resulting in a sound being classified as "cough", and those below being classified as "non-cough.
DEVICE	Hyfe Air	Hyfe Air is a wearable device with an incorporated wireless lapel microphone. The device´s recordings can be run through the same cough-detection algorithm used by Hyfe Cough Tracker, while its results are directly stored in a remote database and are not displayed to participants.

Timeline

• Start date: 2021-09-15
• Primary completion: 2022-09-15
• Completion: 2022-09-15
• First posted: 2021-09-13
• Last updated: 2025-11-20

Locations

1 site across 1 country: Spain