Investigation of Lung Sounds Features for Detection of Bronchitis and COPD Using Machine Learning Methods
Keywords:lung sounds, bronchitis , chronic obstructive pulmonary disease, spectral wavelet decomposition, mel-frequency cepstral analysis, machine learning
The study is dedicated to the issue of investigation of the lung sounds digital analysis processing methods, searching for new informative features of pathological respiratory sounds and using machine learning methods for classifying the state of the bronchopulmonary system. In particular, the use of various methods of lung sounds analysis is considered, namely: frequency, time-frequency, wavelet, and mel-frequency cepstral analysis. The application of signal processing methods to the problem of respiration signals analysis is considered in the paper. In order to investigate the possibilities of machine learning to the problem of classification of respiration signals, the dataset of lung sounds of 296 recordings, which represent 3 classes: norm, bronchitis, and chronic obstructive pulmonary disease, was used in this work. The purpose of this study is to identify and compare the informative features of the lung sounds obtained with different signal processing methods, as well as to choose the classification method that provides the highest accuracy in the identification of the bronchopulmonary system condition. To obtain frequency features, power spectrum density dependence on frequency was calculated for respiratory signals using fast Fourier transform method. The spectral measures, as well as ratios of spectrum powers in different frequency bands, were defined. To extract the spectral-temporal features of the lung sounds, the spectrograms of the analyzed signals were investigated. The mean time dependences of the power spectral density in the indicated frequency ranges were determined. The sum of magnitudes values of the power spectrum curve for each frequency band was used as the features obtained from the spectrogram. The ratios of the energies corresponding to detail levels of the wavelet decomposition to the total energy of the decomposed signal were used as the parameters of signal recognition based on wavelet analysis. The logarithmic (mel) filterbank energies, averaged over time frames, depending on channel index and time, as well as mel frequency cepstrum depending on cepstrum index and time, are proposed to use as features derived from mel-cepstral analysis. The supervised machine learning based on decision trees, discriminant analysis, support vector machines, logistic regression, k-nearest neighbors classifiers, and ensemble learning were applied to determine the best classification models for computerized disease screening. The accuracy of the different classifiers using these feature sets was determined and compared. Based on this, a combination of features and classifiers, which provides the highest accuracy of lung condition recognition, reaching 93%, is proposed.
Lung disease & information. European Lung Foundation.
The top 10 causes of death. (2020). World Health Organization.
Kumar S. S. (2020). Emerging Technologies and Sensors That Can Be Used During the COVID-19 Pandemic. International Conference on UK-China Emerging Technologies (UCET), Glasgow, UK, pp. 1-. DOI: 10.1109/UCET51115.2020.9205424.
GÜLER H. C. et al. (2020). Classification of Abnormal Respiratory Sounds Using Machine Learning Techniques. 2020 Medical Technologies Congress (TIPTEKNO), Antalya, pp. 1-4. DOI: 10.1109/TIPTEKNO50054.2020.9299294.
Chekhovych M. G., Poreva A. S., Timofeyev V. I. and Henaff P. (2018). Using of the machine learning methods to identify bronchopulmonary system diseases with the use of lung sounds. Visnyk NTUU KPI, Seriia Radiotekhnika Radioaparatobuduvannia, No. 73, pp. 55–62. DOI: 10.20535/RADAP.2018.73.55-62.
Serato J. H. L. and Reyes R. (2018). Automated lung auscultation identification for mobile health systems using machine learning. 2018 IEEE International Conference on Applied System Invention (ICASI), Chiba, pp. 287-290. DOI: 10.1109/ICASI.2018.8394589.
Paraschiv E.-A. and Rotaru C.-M. (2020). Machine Learning Approaches based on Wearable Devices for Respiratory Diseases Diagnosis. 2020 International Conference on e-Health and Bioengineering (EHB), Iasi, Romania, pp. 1-4. DOI: 10.1109/EHB50910.2020.9280098.
Sarkar M., Madabhavi I., Niranjan N., Dogra M. (2015). Auscultation of the respiratory system. Annals of Thoracic Medicine, Vol. 10, Iss. 3, pp. 158–168. DOI: 10.4103/1817-1737.160831.
Gottlieb E. R., Aliotta J. M., Tammaro D. (2018). Comparison of analogue and electronic stethoscopes for pulmonary auscultation by internal medicine residents. Postgraduate Medical Journal, Vol. 94, Iss. 1118. DOI: 10.1136/postgradmedj-2018-136052.
Ellington L. E., Gilman R. H., Tielsch J. M., et al, (2012). Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study. BMJ Open, Vol. 2, Iss. 1. DOI: 10.1136/bmjopen-2011-000506.
Pasterkamp H., Brand P. L. P., Everard M., Garcia-Marcos L., Melbye H., Priftis K. N. (2016). Towards the standardisation of lung sound nomenclature. European Respiratory Journal, Vol. 47, Iss. 3, pp. 724-732. DOI: 10.1183/13993003.01132-2015.
Arts L., Lim E.H.T., van de Ven P.M., et al. (2020). The diagnostic accuracy of lung auscultation in adult patients with acute pulmonary pathologies: a meta-analysis. Scientific Reports, Vol. 10, Article number: 7347. DOI: 10.1038/s41598-020-64405-6.
Aras S., Öztürk M., Gangal A. (2016). Endpoint detection of lung sounds for electronic auscultation. 2016 39th International Conference on Telecommunications and Signal Processing (TSP), Vienna, Austria, pp. 405-408. DOI: 10.1109/TSP.2016.7760907.
Tolnai J., Kapus K., Draskóczy M., Bari F., Peták F., Novák Z. (2019). Teleauscultation: an innovative initiative to categorize and analyse lung sounds. European Respiratory Journal, Vol. 54, PA749. DOI: 10.1183/13993003.congress-2019.PA749.
Andrès E., Gass R., Charloux A., Brandt C. and Hentzler A. (2018). Respiratory sound analysis in the era of evidence-based medicine and the world of medicine. Journal of Medicen and Life, Vol. 11, No. 2, pp. 89-106.
Bandyopadhyaya I., Islam M. A., Bhattacharyya P., Saha G. (2017). A novel spectrogram based approach towards automatic lung sound cycle extraction. 2017 IEEE Calcutta Conference (CALCON), Kolkata, pp. 448-451. DOI: 10.1109/CALCON.2017.8280773.
Oliynik V. (2018). Time-Domain Fragment-Similarity Processing of the Auscultation Records for Detection of their Periodic Components. 2018 IEEE 38th International Conference on Electronics and Nanotechnology (ELNANO), Kyiv, Ukraine, pp. 340-345. DOI: 10.1109/ELNANO.2018.8477549.
Pingale T. H. and Patil H. T. (2017). Analysis of Cough Sound for Pneumonia Detection Using Wavelet Transform and Statistical Parameters. 2017 International Conference on Computing, Communication, Control and Automation (ICCUBEA), Pune, India, pp. 1-6. DOI: 10.1109/ICCUBEA.2017.8463900.
Rahmandani M., Nugroho H. A. and Setiawan N. A. (2018). Cardiac Sound Classification Using Mel-Frequency Cepstral Coefficients (MFCC) and Artificial Neural Network (ANN). 2018 3rd International Conference on Information Technology, Information System and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, pp. 22-26. DOI: 10.1109/ICITISEE.2018.8721007.
Sengupta N., Sahidullah Md, Saha G. (2016). Lung sound classification using cepstral-based statistical features. Computers in Biology and Medicine, Vol. 75, pp. 118-129. DOI: 10.1016/j.compbiomed.2016.05.013.
Grinchenko V. T., Makarenkov A. P., Makarenkova A. A. (2010). Computer auscultation is a new method of objectifying the characteristics of breath sounds. Clinical Informatics and Telemedicine, No. 7, p. 31-37. [In Russian].
Grinchenko В. Т., Vinogtadnyi G. P., Makarenkova А. А. (2006). Acoustic Sensor. [AKUSTYChNYI SENSOR]. Patent Of Ukraine [Patent Ukrainy], No. 14732, Bul. № 5, 15.05.2006.
Gross V., Dittmar A., Penzel T., Schüttler F. and Von Wichert P.(1999). The Relationship between Normal Lung Sounds, Age, and Gender. American Journal of Respiratory and Critical Care Medicine, Vol. 162, Iss. 3, pp. 905-909. DOI: 10.1164/ajrccm.162.3.9905104.
Mantji B. N., Oloo F. R. A. (2019). Electronic Stethoscope Design, Prototyping and Testing. IEEE EUROCON 2019 -18th International Conference on Smart Technologies, Novi Sad, Serbia, pp. 1-7. DOI: 10.1109/EUROCON.2019.8861992.
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