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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References
World Health Organization (2015) The top 10 causes of death
Gibson GJ, Loddenkemper R, Lundbäck B, Sibille Y (2013) Respiratory health and disease in Europe: the new European Lung White Book. Eur Respir J 42:559–563
Marques A, Oliveira A, Jácome C (2014) Computerized adventitious respiratory sounds as outcome measures for respiratory therapy: a systematic review. Respir Care 59(5):765–776
Earis J, Cheetham B (2000) Current methods used for computerized respiratory sound analysis. Eur Respir Rev 10(77):586–590
Piirila P, Sovijarvi AR (1995) Crackles: recording, analysis and clinical significance. Eur Respir J 8(12):2139–2148
Sarkar M, Madabhavi I, Niranjan N, Dogra M (2015) Auscultation of the respiratory system. Ann Thorac Med 10(3):158
Sovijarvi ARA, Malmberg LP, Charbonneau G, Vanderschoot J, Dalmasso F, Sacco C, Rossi M, Earis JE (2000) Characteristics of breath sounds and adventitious respiratory sounds. Eur Respir Rev 10:591–596
Pramono RXA, Bowyer S, Rodriguez-Villegas E (2017) Automatic adventitious respiratory sound analysis: a systematic review. PLoS ONE 12(5):e0177926
Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR (2016) Application of semi-supervised deep learning to lung sound analysis. In: 38th annual international conference of IEEE engineering in medicine and biology society, pp 804–807
Rossi M, Sovijarvi ARA, Piirila P, Vannuccini L, Dalmasso F, Vanderschoot J (2000) Environmental and subject conditions and breathing manoeuvres for respiratory sound recordings. Eur Respir Rev 10:611–615
Machado A, Oliveira A, Jácome C, Pereira M, Moreira J, Rodrigues J, Aparício J, Jesus LMT, Marques A (2017) Usability of Computerized Lung Auscultation–Sound Software (CLASS) for learning pulmonary auscultation. Med Biol Eng Comput 1–11
Guntupalli KK, Alapat PM, Bandi VD, Kushnir I (2008) Validation of automatic wheeze detection in patients with obstructed airways and in healthy subjects. J Asthma 45(10):903–907
Dinis J, Guilherme C, Rodrigues J, Marques A (2013) Respiratory sound annotation software. In: International conference on health informatics, pp 183–188
Pinho C, Oliveira A, Jácome C, Rodrigues J, Marques A (2015) Automatic crackle detection algorithm based on fractal dimension and box filtering. Procedia Comput Sci 64:705–12
Lartillot O, Toiviainen PA (2007) Matlab toolbox for musical feature extraction from audio. In: International conference on digital audio effects, pp 237–244
Mendes L, Vogiatzis IM, Perantoni E, Kaimakamis E, Chouvarda I, Maglaveras N, Henriques J, Carvalho P, Paiva RP (2016) Detection of crackle events using a multi-feature approach. In: 38th Annual International Conference of IEEE Engineering in Medicine Biology Soceity, pp 3679–83
Rocha BM, Mendes L, Couceiro R, Henriques J, Carvalho P, Paiva RP (2017) Detection of explosive cough events in audio recordings by internal sound analysis. In: 39th annual international conference of IEEE engineering in medicine biology, pp 2761–2764
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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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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