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Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods

Yıl 2022, , 169 - 180, 30.06.2021
https://doi.org/10.18466/cbayarfbe.1002917

Öz

The computerized respiratory sound analysis systems provide vital information concerning the current condition of the lung. These systems, used by physicians for the diagnosis of diseases, help to classify respiratory sounds. Because each physician has different knowledge and experience, there is a problem with diagnosing and treating respiratory system diseases. This study will help the physician to decide in various difficult diagnostic situations easily. For this purpose, different machine learning classifiers and feature extraction models have been constituted to classify respiratory sounds as healthy and patient then its results were compared. In this study, Empirical Mode Decomposition, Mel Frequency Cepstral Coefficients, and Wavelet Transform methods are used for feature extraction, while k Nearest Neighbor, Artificial Neural Networks, and Support Vector Machines are used for classification. The best accuracy was 98.8% by using combination Mel Frequency Cepstral Coefficient and k Nearest Neighbor methods.

Destekleyen Kurum

Manisa Celal Bayar University Scientific Research Project Coordination Unit

Proje Numarası

2017-191

Kaynakça

  • [1] World Health Organization. The Top 10 Causes of Death n.d. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death (accessed March 20, 2020).
  • [2] Güler I, Polat H, Ergün U. 2005. Combining neural network and genetic algorithm for prediction of lung sounds. Journal of Medical Systems; 29:217–31. doi: 10.1007/s10916-005-5182-9.
  • [3] Polat H, Güler I. 2004. A simple computer-based measurement and analysis system of pulmonary auscultation sounds. Journal of Medical Systems; 28: 665–72. doi: 10.1023/B:JOMS.0000044968.45013.ce.
  • [4] Rocha BM, Filos D, Mendes L, Serbes G, Ulukaya S, Kahya YP, et al. 2019. An open access database for the evaluation of respiratory sound classification algorithms. Physiological Measurement; 40. doi:10.1088/1361-6579/ab03ea.
  • [5] Homs-Corbera A, Fiz JA, Morera J, Jané R. 2004. Time-Frequency Detection and Analysis of Wheezes During Forced Exhalation. IEEE Transactions on Biomedical Engineering; 51: 182–186. doi: 10.1109/TBME.2003.820359.
  • [6] Sezgin MC, Dokur Z, Olmez T, Korurek M. Classification of respiratory sounds by using an artificial neural network. 2001 Proceedings of the 23rd Annual EMBS International Conference, Istanbul, Turkey, 2001, pp. 697-699. doi:10.1109/IEMBS.2001.1019035.
  • [7] Maruf SO, Azhar MU, Khawaja SG, Akram MU. Crackle separation and classification from normal respiratory sounds using Gaussian Mixture Model. 2015 IEEE 10th International Conference on Industrial and Information Systems, Sri Lanka, 2015, pp. 267-271 doi: 10.1109/ICIINFS.2015.7399022
  • [8] Lozano M, Fiz JA, Jané R. 2016. Performance evaluation of the Hilbert-Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization. Signal Processing; 120: 99–116. doi:10.1016/j.sigpro.2015.09.005.
  • [9] Palaniappan R, Sundaraj K, Sundaraj S. 2014. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinformatics; 15:223. doi:10.1186/1471-2105-15-223.
  • [10] Sengupta N, Sahidullah M, Saha G. 2016. Lung sound classification using cepstral-based statistical features. Computers in Biology and Medicine; 75: 118–29. doi: 10.1016/j.compbiomed.2016.05.013.
  • [11] Liu Y, Lin Y, Zhang X, Wang Z, Gao Y, Chen G, et al. Classifying respiratory sounds using electronic stethoscope. 2017 IEEE SmartWorld, Ubiquitous Intelligence& Computing, Advanced& Trusted Computed, Scalable Computing& Communications, Cloud& Big Data Computing, Internet of People and Smart City Innovation, San Francisco, CA, USA, 2017. doi: 10.1109/UIC-ATC.2017.8397496
  • [12] Sunil NKB, Ganesan R. 2015. Adaptive neuro-fuzzy inference system for classification of respiratory signals using cepstral features. International Journal of Applied Engineering Research; 10 (28): 22121-22125.
  • [13] Haider NS, Singh BK, Periyasamy R, Behera AK. 2019. Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. Journal of Medical Systems; 43: 255. doi: 10.1007/s10916-019-1388-0.
  • [14] Pramono RXA, Bowyer S, Rodriguez-Villegas E. 2017. Automatic adventitious respiratory sound analysis: A systematic review. Plos One; 12(5): 1-43. doi: 10.1371/journal.pone.0177926 [15] Bokov P, Mahut B, Flaud P, Delclaux C. 2016. Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population. Computers in Biology and Medicine; 70 (2016): 40-50. doi:19 10.1016/j.compbiomed.2016.01.002.
  • [16] Bahoura M. Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. 2009. Computers in Biology and Medicine; 39 (2009): 824-843. doi: 10.1016/j.compbiomed.2009.06.011
  • [17] Göğüş FZ, Karlık B, Harman G. 2016. Identification of pulmonary disorders by using different spectral analysis methods. International Journal of Computational Intelligence Systems; 9 (4): 595-611. doi:10.1080/18756891.2016.1204110
  • [18] Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N. 2004. Neural classification of lung sounds using wavelet coefficients. Computers in Biology and Medicine; 34 (2004): 523-537. doi: 10.1016/S0010-27 4825(03)00092-1
  • [19] Oweis RJ, Abdulhay EW, Khayal A, Awad A. 2015. An alternative respiratory sounds classification system utilizing artificial neural networks. Biomedical Journal; 38 (2): 153-161. doi: 10.4103/2319-4170.137773.
  • [20] Lozano M, Fiz JA, Jane R. 2016. Automatic differentiation of normal and continuous adventitious respiratory sounds using ensemble empirical mode decomposition and instantaneous frequency. IEEE Journal Of Biomedical And Health Informatics; 20 (2): 486-497. doi: 10.1109/JBHI.2015.2396636
  • [21] Pasterkamp H, Kraman SS, Wodicka GR. 1997. State of the Art Respiratory Sounds Advances Beyond the Stethoscope. American Journal of Respiratory and Critical Care Medicine; 156(3): 974-987. doi: 10.1164/ajr-ccm.156.3.9701115.
  • [22] Palaniappan R, Sundaraj K, Ahamed N, Arjunan A, Sundaraj S. 2013. Computer-based respiratory sound analysis: a systematic review. IETE Technical Review; 30 (3): 248-256. doi: 10.4103/0256-4602.113524
  • [23] Dokur Z. 2009. Respiratory sound classification by using an incremental supervised neural network. Pattern Analysis and Applications; 12 (4): 309-319. doi: 10.1007/s10044-008-0125-y
  • [24] Sovijarvi ARA, Vanderschoot J, Earis JE. 2000. Standardization of computerized respiratory sound analysis. European Respiratory Review; 10 (77): 585.
  • [25] Ben Nouma B, Mitiche A, Ouakrim Y, Mezghani N. 2019. Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals. Machine Learning and Knowledge Extraction; 1 (3): 768-784. doi:10.3390/make1030045
  • [26] Vannuccini L, Earis JE, Helistö P, Cheetham BMG, Rossi M et al. 2000. Capturing and preprocessing of respiratory sounds. European Respiratory Review; 10 (77): 616-620.
  • [27] Huang NE, Shen Z, Long SR,Wu MC, Shih HH et al. 1998. The Empirical Mode Decomposition and The Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis. Royal Society of London Proceedings Series A; 454: 903-995. doi: 10.1098/rspa.1998.0193
  • [28] İçer S, Gengeç Ş. 2014. Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds. Digital Signal Processing; 28 (2014): 18-27. doi: 10.1016/j.dsp.2014.02.001
  • [29] Hu X, Peng S, Hwang WL. 2012. EMD revisited: A new understanding of the envelope and resolving the model mixing problem in AM-FM signals. IEEE Transactions on Signal Processing; 60(3): 1075-1086. doi:13 10.1109/tsp.2011.2179650 [30] Deering R, Kaiser JF. The use of a masking signal to improve Empirical Mode Decomposition. IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA, 2005. doi:10.1109/icassp.2005.1416051
  • [31] Palaniappan R, Sundaraj K. Respiratory sound classification using cepstral features and support vector machine. 2013 IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, India, 2013. doi:10.1109/raics.2013.6745460
  • [32] Aykanat M, Kılıç Ö, Kurt B, Saryal S. 2017. Classification of lung sounds using convolutional neural networks. EURASIP Journal on Image and Video Processing; 2017(1). doi: 10.1186/s13640-017-0213-2
  • [33] Rioul O, Vetterli M. 1991. Wavelets and Signal Processing. IEEE Signal Processing Magazine; 8 (4): 14-38. doi: 10.1109/79.91217
  • [34] Gengeç Ş. Akciğer Seslerinden İşaret İşleme Teknikleri Kullanılarak Özellik Çıkarma ve Sınıflandırma. Erciyes Üniversitesi, 2012. (in Turkish)
  • [35] Subasi A. 2007. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications; 32 (4): 1084-1093. doi:10.1016/j.eswa.2006.02.005
  • [36] Reichert S, Gass R, Brandt C, Andres E. 2008. Analysis of Respiratory Sounds: State of the Art. Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine; 2008 (2): 45-58. doi: 10.4137/ccrpm.s530
  • [37] Vapnik VN. Statistical Learning Theory. New York: Wiley; 1998.
  • [38] Başer F, Apaydın A. 2015. Comparison Between Logistic Regression and Support Vector Machines for Classification Purposes. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi B- Teorik Bilimler; 3 (2): 53-65 (in Turkish with an abstract in English) doi: 10.20290/btdb.67263.
  • [39] Naves R, Barbosa BHG, Ferreira DD. 2016. Classification of lung sounds using higher-order statistics: A divide-and-conquer approach. Computer Methods and Programs in Biomedicine; 129 (2016): 12-20. doi:36 10.1016/j.cmpb.2016.02.013
  • [40] Bhatia N, Vandana. 2010. Survey of Nearest Neighbor Condensing Techniques. International Journal of Computer Science and Information Security; 8 (2): 302-305.
  • [41] Tu J V. 1996. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology; 49 (11):1225-1231. doi: 10.1016/s0895-4356(96)00002-9
  • [42] Özmen Ö, Khdr A, Avcı E. 2018. Sınıflandırıcıların kalp hastalığı verileri üzerine performans karşılaştırması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi; 30(3): 153–159. (in Turkish with an abstract in English) doi:10.13140/rg.2.2.24732.95365.
Yıl 2022, , 169 - 180, 30.06.2021
https://doi.org/10.18466/cbayarfbe.1002917

Öz

Proje Numarası

2017-191

Kaynakça

  • [1] World Health Organization. The Top 10 Causes of Death n.d. https://www.who.int/news-room/fact-sheets/detail/the-top-10-causes-of-death (accessed March 20, 2020).
  • [2] Güler I, Polat H, Ergün U. 2005. Combining neural network and genetic algorithm for prediction of lung sounds. Journal of Medical Systems; 29:217–31. doi: 10.1007/s10916-005-5182-9.
  • [3] Polat H, Güler I. 2004. A simple computer-based measurement and analysis system of pulmonary auscultation sounds. Journal of Medical Systems; 28: 665–72. doi: 10.1023/B:JOMS.0000044968.45013.ce.
  • [4] Rocha BM, Filos D, Mendes L, Serbes G, Ulukaya S, Kahya YP, et al. 2019. An open access database for the evaluation of respiratory sound classification algorithms. Physiological Measurement; 40. doi:10.1088/1361-6579/ab03ea.
  • [5] Homs-Corbera A, Fiz JA, Morera J, Jané R. 2004. Time-Frequency Detection and Analysis of Wheezes During Forced Exhalation. IEEE Transactions on Biomedical Engineering; 51: 182–186. doi: 10.1109/TBME.2003.820359.
  • [6] Sezgin MC, Dokur Z, Olmez T, Korurek M. Classification of respiratory sounds by using an artificial neural network. 2001 Proceedings of the 23rd Annual EMBS International Conference, Istanbul, Turkey, 2001, pp. 697-699. doi:10.1109/IEMBS.2001.1019035.
  • [7] Maruf SO, Azhar MU, Khawaja SG, Akram MU. Crackle separation and classification from normal respiratory sounds using Gaussian Mixture Model. 2015 IEEE 10th International Conference on Industrial and Information Systems, Sri Lanka, 2015, pp. 267-271 doi: 10.1109/ICIINFS.2015.7399022
  • [8] Lozano M, Fiz JA, Jané R. 2016. Performance evaluation of the Hilbert-Huang transform for respiratory sound analysis and its application to continuous adventitious sound characterization. Signal Processing; 120: 99–116. doi:10.1016/j.sigpro.2015.09.005.
  • [9] Palaniappan R, Sundaraj K, Sundaraj S. 2014. A comparative study of the svm and k-nn machine learning algorithms for the diagnosis of respiratory pathologies using pulmonary acoustic signals. BMC Bioinformatics; 15:223. doi:10.1186/1471-2105-15-223.
  • [10] Sengupta N, Sahidullah M, Saha G. 2016. Lung sound classification using cepstral-based statistical features. Computers in Biology and Medicine; 75: 118–29. doi: 10.1016/j.compbiomed.2016.05.013.
  • [11] Liu Y, Lin Y, Zhang X, Wang Z, Gao Y, Chen G, et al. Classifying respiratory sounds using electronic stethoscope. 2017 IEEE SmartWorld, Ubiquitous Intelligence& Computing, Advanced& Trusted Computed, Scalable Computing& Communications, Cloud& Big Data Computing, Internet of People and Smart City Innovation, San Francisco, CA, USA, 2017. doi: 10.1109/UIC-ATC.2017.8397496
  • [12] Sunil NKB, Ganesan R. 2015. Adaptive neuro-fuzzy inference system for classification of respiratory signals using cepstral features. International Journal of Applied Engineering Research; 10 (28): 22121-22125.
  • [13] Haider NS, Singh BK, Periyasamy R, Behera AK. 2019. Respiratory sound based classification of chronic obstructive pulmonary disease: a risk stratification approach in machine learning paradigm. Journal of Medical Systems; 43: 255. doi: 10.1007/s10916-019-1388-0.
  • [14] Pramono RXA, Bowyer S, Rodriguez-Villegas E. 2017. Automatic adventitious respiratory sound analysis: A systematic review. Plos One; 12(5): 1-43. doi: 10.1371/journal.pone.0177926 [15] Bokov P, Mahut B, Flaud P, Delclaux C. 2016. Wheezing recognition algorithm using recordings of respiratory sounds at the mouth in a pediatric population. Computers in Biology and Medicine; 70 (2016): 40-50. doi:19 10.1016/j.compbiomed.2016.01.002.
  • [16] Bahoura M. Pattern recognition methods applied to respiratory sounds classification into normal and wheeze classes. 2009. Computers in Biology and Medicine; 39 (2009): 824-843. doi: 10.1016/j.compbiomed.2009.06.011
  • [17] Göğüş FZ, Karlık B, Harman G. 2016. Identification of pulmonary disorders by using different spectral analysis methods. International Journal of Computational Intelligence Systems; 9 (4): 595-611. doi:10.1080/18756891.2016.1204110
  • [18] Kandaswamy A, Kumar CS, Ramanathan RP, Jayaraman S, Malmurugan N. 2004. Neural classification of lung sounds using wavelet coefficients. Computers in Biology and Medicine; 34 (2004): 523-537. doi: 10.1016/S0010-27 4825(03)00092-1
  • [19] Oweis RJ, Abdulhay EW, Khayal A, Awad A. 2015. An alternative respiratory sounds classification system utilizing artificial neural networks. Biomedical Journal; 38 (2): 153-161. doi: 10.4103/2319-4170.137773.
  • [20] Lozano M, Fiz JA, Jane R. 2016. Automatic differentiation of normal and continuous adventitious respiratory sounds using ensemble empirical mode decomposition and instantaneous frequency. IEEE Journal Of Biomedical And Health Informatics; 20 (2): 486-497. doi: 10.1109/JBHI.2015.2396636
  • [21] Pasterkamp H, Kraman SS, Wodicka GR. 1997. State of the Art Respiratory Sounds Advances Beyond the Stethoscope. American Journal of Respiratory and Critical Care Medicine; 156(3): 974-987. doi: 10.1164/ajr-ccm.156.3.9701115.
  • [22] Palaniappan R, Sundaraj K, Ahamed N, Arjunan A, Sundaraj S. 2013. Computer-based respiratory sound analysis: a systematic review. IETE Technical Review; 30 (3): 248-256. doi: 10.4103/0256-4602.113524
  • [23] Dokur Z. 2009. Respiratory sound classification by using an incremental supervised neural network. Pattern Analysis and Applications; 12 (4): 309-319. doi: 10.1007/s10044-008-0125-y
  • [24] Sovijarvi ARA, Vanderschoot J, Earis JE. 2000. Standardization of computerized respiratory sound analysis. European Respiratory Review; 10 (77): 585.
  • [25] Ben Nouma B, Mitiche A, Ouakrim Y, Mezghani N. 2019. Pattern Classification by the Hotelling Statistic and Application to Knee Osteoarthritis Kinematic Signals. Machine Learning and Knowledge Extraction; 1 (3): 768-784. doi:10.3390/make1030045
  • [26] Vannuccini L, Earis JE, Helistö P, Cheetham BMG, Rossi M et al. 2000. Capturing and preprocessing of respiratory sounds. European Respiratory Review; 10 (77): 616-620.
  • [27] Huang NE, Shen Z, Long SR,Wu MC, Shih HH et al. 1998. The Empirical Mode Decomposition and The Hilbert Spectrum for Nonlinear and Non-stationary Time Series Analysis. Royal Society of London Proceedings Series A; 454: 903-995. doi: 10.1098/rspa.1998.0193
  • [28] İçer S, Gengeç Ş. 2014. Classification and analysis of non-stationary characteristics of crackle and rhonchus lung adventitious sounds. Digital Signal Processing; 28 (2014): 18-27. doi: 10.1016/j.dsp.2014.02.001
  • [29] Hu X, Peng S, Hwang WL. 2012. EMD revisited: A new understanding of the envelope and resolving the model mixing problem in AM-FM signals. IEEE Transactions on Signal Processing; 60(3): 1075-1086. doi:13 10.1109/tsp.2011.2179650 [30] Deering R, Kaiser JF. The use of a masking signal to improve Empirical Mode Decomposition. IEEE International Conference on Acoustics, Speech, and Signal Processing, Philadelphia, PA, USA, 2005. doi:10.1109/icassp.2005.1416051
  • [31] Palaniappan R, Sundaraj K. Respiratory sound classification using cepstral features and support vector machine. 2013 IEEE Recent Advances in Intelligent Computational Systems, Trivandrum, India, 2013. doi:10.1109/raics.2013.6745460
  • [32] Aykanat M, Kılıç Ö, Kurt B, Saryal S. 2017. Classification of lung sounds using convolutional neural networks. EURASIP Journal on Image and Video Processing; 2017(1). doi: 10.1186/s13640-017-0213-2
  • [33] Rioul O, Vetterli M. 1991. Wavelets and Signal Processing. IEEE Signal Processing Magazine; 8 (4): 14-38. doi: 10.1109/79.91217
  • [34] Gengeç Ş. Akciğer Seslerinden İşaret İşleme Teknikleri Kullanılarak Özellik Çıkarma ve Sınıflandırma. Erciyes Üniversitesi, 2012. (in Turkish)
  • [35] Subasi A. 2007. EEG signal classification using wavelet feature extraction and a mixture of expert model. Expert Systems with Applications; 32 (4): 1084-1093. doi:10.1016/j.eswa.2006.02.005
  • [36] Reichert S, Gass R, Brandt C, Andres E. 2008. Analysis of Respiratory Sounds: State of the Art. Clinical Medicine: Circulatory, Respiratory and Pulmonary Medicine; 2008 (2): 45-58. doi: 10.4137/ccrpm.s530
  • [37] Vapnik VN. Statistical Learning Theory. New York: Wiley; 1998.
  • [38] Başer F, Apaydın A. 2015. Comparison Between Logistic Regression and Support Vector Machines for Classification Purposes. Anadolu Üniversitesi Bilim ve Teknoloji Dergisi B- Teorik Bilimler; 3 (2): 53-65 (in Turkish with an abstract in English) doi: 10.20290/btdb.67263.
  • [39] Naves R, Barbosa BHG, Ferreira DD. 2016. Classification of lung sounds using higher-order statistics: A divide-and-conquer approach. Computer Methods and Programs in Biomedicine; 129 (2016): 12-20. doi:36 10.1016/j.cmpb.2016.02.013
  • [40] Bhatia N, Vandana. 2010. Survey of Nearest Neighbor Condensing Techniques. International Journal of Computer Science and Information Security; 8 (2): 302-305.
  • [41] Tu J V. 1996. Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes. Journal of Clinical Epidemiology; 49 (11):1225-1231. doi: 10.1016/s0895-4356(96)00002-9
  • [42] Özmen Ö, Khdr A, Avcı E. 2018. Sınıflandırıcıların kalp hastalığı verileri üzerine performans karşılaştırması. Fırat Üniversitesi Mühendislik Bilimleri Dergisi; 30(3): 153–159. (in Turkish with an abstract in English) doi:10.13140/rg.2.2.24732.95365.
Toplam 40 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Burcu Acar Demirci 0000-0002-7328-1267

Yücel Koçyiğit 0000-0003-1785-198X

Deniz Kızılırmak 0000-0001-9445-1598

Yavuz Havlucu 0000-0002-7441-288X

Proje Numarası 2017-191
Yayımlanma Tarihi 30 Haziran 2021
Yayımlandığı Sayı Yıl 2022

Kaynak Göster

APA Acar Demirci, B., Koçyiğit, Y., Kızılırmak, D., Havlucu, Y. (2021). Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods. Celal Bayar University Journal of Science, 18(2), 169-180. https://doi.org/10.18466/cbayarfbe.1002917
AMA Acar Demirci B, Koçyiğit Y, Kızılırmak D, Havlucu Y. Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods. CBUJOS. Haziran 2021;18(2):169-180. doi:10.18466/cbayarfbe.1002917
Chicago Acar Demirci, Burcu, Yücel Koçyiğit, Deniz Kızılırmak, ve Yavuz Havlucu. “Adventitious and Normal Respiratory Sound Analysis With Machine Learning Methods”. Celal Bayar University Journal of Science 18, sy. 2 (Haziran 2021): 169-80. https://doi.org/10.18466/cbayarfbe.1002917.
EndNote Acar Demirci B, Koçyiğit Y, Kızılırmak D, Havlucu Y (01 Haziran 2021) Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods. Celal Bayar University Journal of Science 18 2 169–180.
IEEE B. Acar Demirci, Y. Koçyiğit, D. Kızılırmak, ve Y. Havlucu, “Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods”, CBUJOS, c. 18, sy. 2, ss. 169–180, 2021, doi: 10.18466/cbayarfbe.1002917.
ISNAD Acar Demirci, Burcu vd. “Adventitious and Normal Respiratory Sound Analysis With Machine Learning Methods”. Celal Bayar University Journal of Science 18/2 (Haziran 2021), 169-180. https://doi.org/10.18466/cbayarfbe.1002917.
JAMA Acar Demirci B, Koçyiğit Y, Kızılırmak D, Havlucu Y. Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods. CBUJOS. 2021;18:169–180.
MLA Acar Demirci, Burcu vd. “Adventitious and Normal Respiratory Sound Analysis With Machine Learning Methods”. Celal Bayar University Journal of Science, c. 18, sy. 2, 2021, ss. 169-80, doi:10.18466/cbayarfbe.1002917.
Vancouver Acar Demirci B, Koçyiğit Y, Kızılırmak D, Havlucu Y. Adventitious and Normal Respiratory Sound Analysis with Machine Learning Methods. CBUJOS. 2021;18(2):169-80.