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

Gökhan Altan; Yakup Kutlu; Yusuf Garbi; Adnan Özhan Pekmezci; Serkan Nural

doi:10.28978/nesciences.349282

Research Article

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

Year 2017, Volume: 2 Issue: 3, 59 - 72, 30.10.2017

Gökhan Altan Yakup Kutlu Yusuf Garbi Adnan Özhan Pekmezci Serkan Nural

https://doi.org/10.28978/nesciences.349282

Cited By: 14

Abstract

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.

Keywords

Auscultation, lung and heart sounds, chest X-ray, spirometry, Respiratory database, Chronic Obstructive Pulmonary Disease, asthma, bronchitis

References

Celli, B. R., MacNee, W., Agusti, A., Anzueto, A., Berg, B., Buist, A. S., Calverley, P.M.A., Chavannes, N., Dillard, T., Fahy, B., Fein, A., Heffner, J., Lareau, S., Meek, P., Martinez, F., McNicholas, W., Muris, J., Austegard, E., Pauwels, R., Rennard, S., Rossi, A., Siafakas, N., Tiep, B., Vestbo, J., Wouters, E., & 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/

Year 2017, Volume: 2 Issue: 3, 59 - 72, 30.10.2017

Gökhan Altan Yakup Kutlu Yusuf Garbi Adnan Özhan Pekmezci Serkan Nural

https://doi.org/10.28978/nesciences.349282

Cited By: 14

Abstract

References

Celli, B. R., MacNee, W., Agusti, A., Anzueto, A., Berg, B., Buist, A. S., Calverley, P.M.A., Chavannes, N., Dillard, T., Fahy, B., Fein, A., Heffner, J., Lareau, S., Meek, P., Martinez, F., McNicholas, W., Muris, J., Austegard, E., Pauwels, R., Rennard, S., Rossi, A., Siafakas, N., Tiep, B., Vestbo, J., Wouters, E., & 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/

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Details

Subjects	Engineering
Journal Section	2
Authors	Gökhan Altan Yakup Kutlu Yusuf Garbi This is me Adnan Özhan Pekmezci This is me Serkan Nural This is me
Publication Date	October 30, 2017
Submission Date	November 4, 2017
Published in Issue	Year 2017 Volume: 2 Issue: 3

Cite

APA	Altan, G., Kutlu, Y., Garbi, Y., Pekmezci, A. Ö., et al. (2017). Multimedia Respiratory Database (RespiratoryDatabase@TR): Auscultation Sounds and Chest X-rays. Natural and Engineering Sciences, 2(3), 59-72. https://doi.org/10.28978/nesciences.349282