Classification for long-term monitoring of cough. Case Study

den Brinker, Albertus C.; Rietman, Ronald; Ouweltjes, Okke; van Marion, Matthijs; Thackray-Nocera, Susannah; Crooks, Michael G.; Morice, Alyn H.

Abstract

For management of chronic respiratory diseases, unobtrusive longitudinal monitoring of cough has been proposed. Such a monitoring system was developed using a classifier trained on an initial observation period. After this initial period, a personalized system is available being optimized for the patient and the particular acoustic environment. Long-term deployment of the system requires that the extracted features and learned model characterizing the coughs (and its environment) are time-invariant. This is studied by an example using annotation of two largely different epochs. The results suggest that time-invariance of the cough sound is sufficiently guaranteed for practical deployment, but that changing acoustic environmental conditions may be a factor to reckon with. Cues for detecting changing situations are discussed.

Citation

den Brinker, A. C., Rietman, R., Ouweltjes, O., van Marion, M., Thackray-Nocera, S., Crooks, M. G., & Morice, A. H. (2025). Classification for long-term monitoring of cough. Case Study. Discover Artificial Intelligence, 5, Article 56. https://doi.org/10.1007/s44163-025-00264-2

• Journal Article Type: Article
• Acceptance Date: Apr 10, 2025
• Online Publication Date: May 12, 2025
• Publication Date: 2025
• Deposit Date: May 27, 2025
• Publicly Available Date: May 28, 2025
• Electronic ISSN: 2731-0809
• Publisher: Springer
• Peer Reviewed: Peer Reviewed
• Volume: 5
• Article Number: 56
• DOI: https://doi.org/10.1007/s44163-025-00264-2
• Public URL: https://hull-repository.worktribe.com/output/5182959