Published January 1, 2019
| Version v1
Journal article
Open
An open access database for the evaluation of respiratory sound classification algorithms
Creators
- Rocha, Bruno M.1
- Filos, Dimitris2
- Mendes, Luis
- Serbes, Gorkem3
- Ulukaya, Sezer
- Kahya, Yasemin P.4
- Jakovljevic, Niksa5
- Turukalo, Tatjana L.5
- Vogiatzis, Ioannis M.2
- Perantoni, Eleni2
- Kaimakamis, Evangelos2
- Natsiavas, Pan Tells2
- Oliveira, Ana
- Jacome, Cristina
- Marques, Alda
- Maglaveras, Nicos
- Paiva, Rui Pedro1
- Chouvarda, Ioanna2
- de Carvalho, Paulo1
- 1. Univ Coimbra, Ctr Informat & Syst CISUC, Dept Informat Engn, Coimbra, Portugal
- 2. Aristotle Univ Thessaloniki, Lab Comp Med Informat & Biomed Imaging Technol, Thessaloniki, Greece
- 3. Yildiz Tech Univ, Dept Biomed Engn, TR-34220 Istanbul, Turkey
- 4. Bogazici Univ, Dept Elect & Elect Engn, TR-34342 Istanbul, Turkey
- 5. Univ Novi Sad, Dept Power Elect & Commun Engn, Fac Tech Sci, Novi Sad, Serbia
Description
Objective: Over the last few decades, there has been significant interest in the automatic analysis of respiratory sounds. However, currently there are no publicly available large databases with which new algorithms can be evaluated and compared. Further developments in the field are dependent on the creation of such databases. Approach: This paper describes a public respiratory sound database, which was compiled for an international competition, the first scientific challenge of the IFMBE' s International Conference on Biomedical and Health Informatics. The database includes 920 recordings acquired from 126 participants and two sets of annotations. One set contains 6898 annotated respiratory cycles, some including crackles, wheezes, or a combination of both, and some with no adventitious respiratory sounds. In the other set, precise locations of 10 775 events of crackles and wheezes were annotated. Main results: The best system that participated in the challenge achieved an average score of 52.5% with the respiratory cycle annotations and an average score of 91.2% with the event annotations. Significance: The creation and public release of this database will be useful to the research community and could bring attention to the respiratory sound classification problem.
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