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Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays

Gokhan Altan*, Yakup Kutlu, Yusuf Garbi, Adnan Özhan Pekmezci, Serkan Nural

DOI: 10.28978/nesciences.349282


Auscultation is a method for diagnosis of especially internal medicine diseases such as cardiac, pulmonary and cardio-pulmonary by listening the internal sounds from the body parts. It is the simplest and the most common physical examination in the assessment processes of the clinical skills. In this study, the lung and heart sounds are recorded synchronously from left and right sides of posterior and anterior chest wall and back using two digital stethoscopes in Antakya State Hospital. The chest X-rays and the pulmonary function test variables and spirometric curves, the St. George respiratory questionnaire (SGRQ-C) are collected as multimedia and clinical functional analysis variables of the patients. The 4 channels of heart sounds are focused on aortic, pulmonary, tricuspid and mitral areas. The 12 channels of lung sounds are focused on upper lung, middle lung, lower lung and costophrenic angle areas of posterior and anterior sides of the chest. The recordings are validated and labelled by two pulmonologists evaluating the collected chest x-ray, PFT and auscultation sounds of the subjects. The database consists of 30 healthy subjects and 45 subjects with pulmonary diseases such as asthma, chronic obstructive pulmonary disease, bronchitis. The novelties of the database are the combination ability between auscultation sound results, chest X-ray and PFT; synchronously assessment capability of the lungs sounds; image processing based computerized analysis of the respiratory using chest X-ray and providing opportunity for improving analysis of both lung sounds and heart sounds on pulmonary and cardiac diseases.


Auscultation, lung sounds, heart sounds, chest X-ray, spirometry, Respiratory, pulmonary, Respiratory database, chronic obstructive pulmonary disease, COPD, asthma, bronchitis


Volume 2, No 3, 59-72, 2017

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  • Celli, B. R., MacNee, W., Agusti, A., Anzueto, A., Berg, B., Buist, A. S., … ZuWallack, R. (2004). Standards for the diagnosis and treatment of patients with COPD: A summary of the ATS/ERS position paper. European Respiratory Journal. https://doi.org/10.1183/09031936.04.00014304
  • Decramer, M., Janssens, W., & Miravitlles, M. (2012). Chronic obstructive pulmonary disease. Lancet, 379(9823), 1341–51. https://doi.org/10.1016/S0140-6736(11)60968-9
  • Dokur, Z. (2009). Respiratory sound classification by using an incremental supervised neural network. Pattern Analysis and Applications, 12(4), 309–319. https://doi.org/10.1007/s10044-008-0125-y
  • Friis, B., Eiken, M., Hornsleth, a, & Jensen, a. (1990). Chest X-ray appearances in pneumonia and bronchiolitis. Correlation to virological diagnosis and secretory bacterial findings. Acta Paediatrica Scandinavica, 79(2), 219–25. Retrieved from http://www.ncbi.nlm.nih.gov/pubmed/2321485
  • GBD 2015 Disease and Injury Incidence and Prevalence Collaborators, G. 2015 D. and I. I. and P. (2016). Global, regional, and national incidence, prevalence, and years lived with disability for 310 diseases and injuries, 1990-2015: a systematic analysis for the Global Burden of Disease Study 2015. Lancet (London, England), 388(10053), 1545–1602. https://doi.org/10.1016/S0140-6736(16)31678-6
  • Güler, E. Ç., Sankur, B., Kahya, Y. P., & Raudys, S. (2005). Two-stage classification of respiratory sound patterns. Computers in Biology and Medicine, 35(1), 67–83. https://doi.org/10.1016/j.compbiomed.2003.11.001
  • Hederos, C.-A., Janson, S., Andersson, H., & Hedlin, G. (2004). Chest X-ray investigation in newly discovered asthma. Pediatric Allergy and Immunology : Official Publication of the European Society of Pediatric Allergy and Immunology, 15(2), 163–5. https://doi.org/10.1046/j.1399-3038.2003.00098.x
  • Himeshima, M., Yamashita, M., Matsunaga, S., & Miyahara, S. (2012). Detection of abnormal lung sounds taking into account duration distribution for adventitious sounds. In European Signal Processing Conference (pp. 1821–1825).
  • Homs-Corbera, A., Fiz, J. A., Morera, J., & Jané, R. (2004). Time-Frequency Detection and Analysis of Wheezes during Forced Exhalation. IEEE Transactions on Biomedical Engineering, 51(1), 182–186. https://doi.org/10.1109/TBME.2003.820359
  • Matsutake, S., Yamashita, M., & Matsunaga, S. (2015). Abnormal-respiration detection by considering correlation of observation of adventitious sounds. In 2015 23rd European Signal Processing Conference, EUSIPCO 2015 (pp. 634–638). https://doi.org/10.1109/EUSIPCO.2015.7362460
  • Nakamura, N., Yamashita, M., & Matsunaga, S. (2016). Detection of patients considering observation frequency of continuous and discontinuous adventitious sounds in lung sounds. 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). https://doi.org/10.1109/EMBC.2016.7591472
  • Roisin RR. (2016). Chronic Obstructive Pulmonary Disease Updated 2010 Global Initiative for Chronic Obstructive Lung Disease. Global Initiative for Chronic Obstructive Lung Disease. Inc, 1–94. https://doi.org/10.1097/00008483-200207000-00004
  • Salvi, S. S., & Barnes, P. J. (2009). Chronic obstructive pulmonary disease in non-smokers. The Lancet. https://doi.org/10.1016/S0140-6736(09)61303-9
  • Sovijärvi, A. R. A., Vanderschoot, J., & Earis, J. E. (2000). Standardization of computerized respiratory sound analysis. Eur Respir Rev, 10, 77–585.
  • Troosters, T., Casaburi, R., Gosselink, R., & Decramer, M. (2005). Pulmonary rehabilitation in chronic obstructive pulmonary disease. American Journal of Respiratory and Critical Care Medicine. https://doi.org/10.1164/rccm.200408-1109SO
  • Umeki, S., Yamashita, M., & Matsunaga, S. (2015). Classification between normal and abnormal lung sounds using unsupervised subject-adaptation. 2015 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA). https://doi.org/10.1109/APSIPA.2015.7415506
  • Vaz Fragoso, C. A., Concato, J., McAvay, G., Van Ness, P. H., Rochester, C. L., Yaggi, H. K., & Gill, T. M. (2010). The ratio of FEV1 to FVC as a basis for establishing chronic obstructive pulmonary disease. American Journal of Respiratory and Critical Care Medicine, 181(5), 446–451. https://doi.org/10.1164/rccm.200909-1366OC
  • Waitman, L. R., Clarkson, K. P., Barwise, J. A., & King, P. H. (2000). Representation and classification of breath sounds recorded in an intensive care setting using neural networks. Journal of Clinical Monitoring and Computing, 16(2), 95–105. https://doi.org/10.1023/A:1009934112185
  • WHO. (n.d.). The top 10 causes of death. Retrieved August 7, 2016, from http://www.who.int/mediacentre/factsheets/fs310/en/