Developing a multi-feature fusion model for exacerbation classification in asthma and COPD
Comprehensive experiments and comparisons with existing algorithms highlight the potential of speech-based monitoring for respiratory conditions in real-world settings. The proposed method outperforms state-of-the-art approaches, offering a promising avenue for exacerbation diagnosis and monitoring while potentially reducing the burden on both patients and healthcare providers.
Authors
- Yuyang Yan (ORCID: https://orcid.org/0009-0005-9914-1480)
- Loes van Bemmel
- Frits M.E. Franssen (ORCID: https://orcid.org/0000-0002-1633-6356)
- Visara Urovi (ORCID: https://orcid.org/0000-0003-2817-3950)
- Sami O. Simons (ORCID: https://orcid.org/0000-0002-4296-5076)
Institutions
- Maastricht University Medical Centre (NL)
- Maastricht University (NL)
Publication Details
- Journal
- Computer Methods and Programs in Biomedicine
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1016/j.cmpb.2025.108796
- Citations
- 4
- Primary Topic
- Emotion and Mood Recognition
- Type
- article
- Field-Weighted Citation Impact
- 8.47
Funders
- Nederlandse Organisatie voor Wetenschappelijk Onderzoek
- Longfonds
- Lung Foundation Netherlands