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Α Respiratory Sound Database for the Development of Automated Classification

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Precision Medicine Powered by pHealth and Connected Health (ICBHI 2017)

Abstract

The automatic analysis of respiratory sounds has been a field of great research interest during the last decades. Automated classification of respiratory sounds has the potential to detect abnormalities in the early stages of a respiratory dysfunction and thus enhance the effectiveness of decision making. However, the existence of a publically available large database, in which new algorithms can be implemented, evaluated, and compared, is still lacking and is vital for further developments in the field. In the context of the International Conference on Biomedical and Health Informatics (ICBHI), the first scientific challenge was organized with the main goal of developing algorithms able to characterize respiratory sound recordings derived from clinical and non-clinical environments. The database was created by two research teams in Portugal and in Greece, and it includes 920 recordings acquired from 126 subjects. A total of 6898 respiration cycles were recorded. The cycles were annotated by respiratory experts as including crackles, wheezes, a combination of them, or no adventitious respiratory sounds. The recordings were collected using heterogeneous equipment and their duration ranged from 10 to 90 s. The chest locations from which the recordings were acquired was also provided. Noise levels in some respiration cycles were high, which simulated real life conditions and made the classification process more challenging.

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Notes

  1. 1.

    https://bhichallenge.med.auth.gr/.

  2. 2.

    http://audacity.sourceforge.net/.

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Acknowledgements

The authors would like to thank the health professionals and the patients who have agreed to participate in the data collection process. This work was financially supported by the EU project WELCOME (FP7-ICT-2013-10/611223) and the FEDER/COMPETE/FCT project UID/BIM/04501/2013. Finally, the authors would like to thank IFMBE for endorsing and supporting this scientific challenge.

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Correspondence to B. M. Rocha .

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Rocha, B.M. et al. (2018). Α Respiratory Sound Database for the Development of Automated Classification. In: Maglaveras, N., Chouvarda, I., de Carvalho, P. (eds) Precision Medicine Powered by pHealth and Connected Health. ICBHI 2017. IFMBE Proceedings, vol 66. Springer, Singapore. https://doi.org/10.1007/978-981-10-7419-6_6

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  • DOI: https://doi.org/10.1007/978-981-10-7419-6_6

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