Auscultation is a classic method of examining patients with respiratory and cardiovascular pathologies. Auscultation is a subjective method, its diagnostic accuracy is highly dependent on the doctor’s experience. Electronic stethoscopes can increase the volume of audio recordings, eliminate noise, and store and transmit sound to a computer or smartphone. Wavelet transform, Butterworth filter, low and high pass filters are used to filter the resulting audio recordings. Machine learning methods, which often surpass to experienced doctors in accuracy, are used to identify various sounds. Methods of mathematical analysis make it possible to differentiate pathological sounds from and innocent heart murmurs, wheezing in the lungs, asthmatic breathing and other pathologies. This review describes various studies on the diagnosis of respiratory and cardiovascular pathologies based on auscultation data.
References
1. Watrous RL, Thompson WR, Ackerman SJ. The impact of computer-assisted auscultation on physician referrals of asymptomatic patients with heart murmurs. Clinical Cardiology: An International Indexed and Peer-Reviewed Journal for Advances in the Treatment of Cardiovascular Disease. 2008; 31(2): 79-83. doi: 10.1002/clc.20185.
2. Andrisevic N, Ejaz K, Rios-Gutierrez F, Alba-Flores R, Nordehn G, Burns S. Detection of heart murmurs using wavelet analysis and artificial neural networks. Journal of Biomechanical Engineering. 2005; 127(6): 899-904. doi: 10.1115/1.2049327.
3. Soto-Murillo MA, Galván-Tejada JI, Galván-Tejada CE, Celaya-Padilla JM, Luna-García H, Magallanes-Quintanar R, et al. Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods. Healthcare. 2021; 9(3): 317. doi: 10.3390/healthcare9030317.
4. Gündüz AF, Karci A. Heart Sound Classification for Murmur Abnormality Detection Using an Ensemble Approach Based on Traditional Classifiers and Feature Sets. Computer Science. 2020; 5(1): 1-13.
5. Chorba JS, Shapiro AM, Le L, Maidens J, Prince J, Pham S, et al. Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform. Journal of the American Heart Association. 2021; 10(9): e019905. doi:10.1161/JAHA.120.019905.
6. Lv J, Dong B, Lei H, Shi G, Wang H, Zhu F, et al. Artificial intelligence-assisted auscultation in detecting congenital heart disease. European Heart Journal. 2021; 2(1): 119-124. doi: 10.1093/ehjdh/ztaa017.
7. Latif S, Usman M, Rana JQ. Abnormal heartbeat detection using recurrent neural networks. Computer Vision and Pattern Recognition. 2018; 1: 1-8.
8. Kang SH, Joe B, Yoon Y, Cho GY, Shin I, Suh JW. Cardiac Auscultation Using Smartphones: Pilot Study. Journal of Medical Internet Research Mhealth and Uhealth. 2018; 6(2): e8946. doi: 10.2196/mhealth.8946.
9. Castello-Herbreteau B, Vaillant MC, Magontier N, Pottier JM, Blond MH, Chantepie A. Diagnostic value of physical examination and electrocardiogram in the initial evaluation of heart murmurs in children. Archives de Pédiatrie. 2000; 7: 1041-1049. doi: 10.1016/s0929-693 x( 00)00311-0.
10. Kumar K, Thompson WR. Evaluation of cardiac auscultation skills in pediatric residents. Clinical Pediatrics. 2013; 52: 66-73. doi: 10.1177/0009922812466584.
11. Pretorius E, Cronje ML, Strydom O. Development of a pediatric cardiac computer aided auscultation decision support system. Annual International Conference of the IEEE Engineering in Medicine and Biology. 2010: 6078-6082. doi: 10.1109/IEMBS.2010.5627633.
12. Wang J, You T, Yi K, Gong Y, Xie Q, Qu F, et al. Intelligent diagnosis of heart murmurs in children with congenital heart disease. Journal of healthcare engineering. 2020; 2020: 9640821. doi: 10.1155/2020/9640821.
13. Gharehbaghi A, Sepehri AA, Babic A. Forth Heart Sound Detection Using Backward Time-Growing Neural Network. CMBEBIH. 2019; 73: 341-345. doi: 10.1007/978-3-030-17971-7_53.
14. Gavrovska A, Zajić G, Bogdanović V, Reljin I, Reljin B. Paediatric heart sound signal analysis towards classification using multifractal spectra. Physiological measurement. 2016; 37(9): 1556. doi: 10.1088/0967-3334/37/9/1556.
15. Kang S, Doroshow R, McConnaughey J, Shekhar R. Automated Identification of Innocent Still’s Murmur in Children. IEEE Transactions on Biomedical Engineering. 2017; 64(6): 1326-1334. doi: 10.1109/TBME.2016.2603787.
16. Zaitseva EG, Chernetsky MV, Shevel NA. About Possibility of Remote Diagnostics of the Respiratory System by Auscultation. Devices and Methods of Measurements. 2020; 11(2): 148-154. doi: 10.21122/2220-9506-2020-11-2-148-154.
17. Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR. Application of semi-supervised deep learning to lung sound analysis. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2016: 804-807. doi: 10.1109/EMBC.2016.7590823.
18. Kim Y, Hyon Y, Jung SS, Lee S, Yoo G, Chung C, et al. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Scientific Reports. 2021; 11: 1-11. doi: 10.1038/s41598-021-96724-7.
19. Altan G, Kutlu Y, Pekmezci AÖ, Nural S. Deep learning with 3D-second order difference plot on respiratory sounds. Biomedical Signal Processing and Control. 2018; 45: 58-69. doi:10.1016/j.bspc.2018.05.014.
20. Berdibaeva G К , Bodin ON, Firsov DS. Classification of sounds of asthmatic breathing using neural networks. Measuring. Monitoring. Management. Control. 2018; 2(24): 86-90. ( In Russ. ). doi: 10.21685/2307-5538-2018-2-11.
21. Demir F, Sengur A, Bajaj V. Convolutional neural networks based efficient approach for classification of lung diseases. Health Information Science and Systems. 2020; 8(4): 1-8. doi: 10.1007/s13755-019-0091-3.
22. Naves R, Barbosa BH, Ferreira DD. Classification of lung sounds using higher-order statistics: A divide-and-conquer approach. Computer Methods and Programs in Biomedicine. 2016; 129: 12-20. doi: 10.1016/j.cmpb.2016.02.013.
23. Poreva AS, Vaityshyn VI, Karplyuk YeS. Machine learning methods for the study of the lungsounds signals. Microsystems, Electronics and Acoustics. 2017; 22(6): 41-47. ( In Russ. ). doi: 10.20535/2523-4455.2017.22.6.108829.
24. Bardou D, Zhang K, Ahmad SM. Lung sounds classification using convolutional neural networks. Artificial Intelligence in Medicine. 2018; 88: 58-69. doi: 10.1016/j.artmed.2018.04.008.
25. Aykanat M, Kılıç Ö, Kurt B, Saryal S. Classification of lung sounds using convolutional neural networks. Journal on Image and Video Processing. 2017; 1: 1-9. doi: 10.1186/s13640-017-0213-2.
26. Zhang J, Wang HS, Zhou HY, Dong B, Zhang L, Zhang F, et al. Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children. Frontiers in Pediatrics. 2021; 9: 152. doi : 10.3389/fped.2021.627337.
27. Grzywalski T, Piecuch M, Szajek M, Bręborowicz A, Hafke-Dys H, Kociński J, et al. Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination. European Journal of Pediatrics. 2019; 178(6): 883-890. doi : 10.1007/s00431-019-03363-2.
28. Furman E, Charushin A, Eirikh E, Furman G, Sokolovsky V, Malinin S, et al. Capabilities of computer analysis of breath sounds in patients with COVID-19. Perm Medical Journal. 2021; 38(3): 97-109. ( In Russ. ). doi : 10.17816/pmj38397%109.
29. Lapteva EA, Kharevich ON, Khatsko VV, Voronova NA, Chamko MV, Bezruchko IV, et al. Automated lung sound analysis using the LungPass platform: A sensitive and specific tool for identifying lower respiratory tract involvement in COVID-19. European Respiratory Journal. 2021; 58(6): 2101907. doi : 10.1183/13993003.01907-2021.
30. Lapteva EA, Kovalenko IV, Laptev AN, Katibnikova EI, Pozdnyakova AS, Korovkin VS, et al. Application of the neural network technology for detection and monitoring of auscultative phenomena in diagnosis and treatment of diseases of the respiratory system. Journal of the Grodno State Medical University. 2020; 18(3): 230-235. ( In Russ. ). doi : 10.25298/2221-8785-2020-18-3-230-235.
2. Andrisevic N, Ejaz K, Rios-Gutierrez F, Alba-Flores R, Nordehn G, Burns S. Detection of heart murmurs using wavelet analysis and artificial neural networks. Journal of Biomechanical Engineering. 2005; 127(6): 899-904. doi: 10.1115/1.2049327.
3. Soto-Murillo MA, Galván-Tejada JI, Galván-Tejada CE, Celaya-Padilla JM, Luna-García H, Magallanes-Quintanar R, et al. Automatic Evaluation of Heart Condition According to the Sounds Emitted and Implementing Six Classification Methods. Healthcare. 2021; 9(3): 317. doi: 10.3390/healthcare9030317.
4. Gündüz AF, Karci A. Heart Sound Classification for Murmur Abnormality Detection Using an Ensemble Approach Based on Traditional Classifiers and Feature Sets. Computer Science. 2020; 5(1): 1-13.
5. Chorba JS, Shapiro AM, Le L, Maidens J, Prince J, Pham S, et al. Deep Learning Algorithm for Automated Cardiac Murmur Detection via a Digital Stethoscope Platform. Journal of the American Heart Association. 2021; 10(9): e019905. doi:10.1161/JAHA.120.019905.
6. Lv J, Dong B, Lei H, Shi G, Wang H, Zhu F, et al. Artificial intelligence-assisted auscultation in detecting congenital heart disease. European Heart Journal. 2021; 2(1): 119-124. doi: 10.1093/ehjdh/ztaa017.
7. Latif S, Usman M, Rana JQ. Abnormal heartbeat detection using recurrent neural networks. Computer Vision and Pattern Recognition. 2018; 1: 1-8.
8. Kang SH, Joe B, Yoon Y, Cho GY, Shin I, Suh JW. Cardiac Auscultation Using Smartphones: Pilot Study. Journal of Medical Internet Research Mhealth and Uhealth. 2018; 6(2): e8946. doi: 10.2196/mhealth.8946.
9. Castello-Herbreteau B, Vaillant MC, Magontier N, Pottier JM, Blond MH, Chantepie A. Diagnostic value of physical examination and electrocardiogram in the initial evaluation of heart murmurs in children. Archives de Pédiatrie. 2000; 7: 1041-1049. doi: 10.1016/s0929-693 x( 00)00311-0.
10. Kumar K, Thompson WR. Evaluation of cardiac auscultation skills in pediatric residents. Clinical Pediatrics. 2013; 52: 66-73. doi: 10.1177/0009922812466584.
11. Pretorius E, Cronje ML, Strydom O. Development of a pediatric cardiac computer aided auscultation decision support system. Annual International Conference of the IEEE Engineering in Medicine and Biology. 2010: 6078-6082. doi: 10.1109/IEMBS.2010.5627633.
12. Wang J, You T, Yi K, Gong Y, Xie Q, Qu F, et al. Intelligent diagnosis of heart murmurs in children with congenital heart disease. Journal of healthcare engineering. 2020; 2020: 9640821. doi: 10.1155/2020/9640821.
13. Gharehbaghi A, Sepehri AA, Babic A. Forth Heart Sound Detection Using Backward Time-Growing Neural Network. CMBEBIH. 2019; 73: 341-345. doi: 10.1007/978-3-030-17971-7_53.
14. Gavrovska A, Zajić G, Bogdanović V, Reljin I, Reljin B. Paediatric heart sound signal analysis towards classification using multifractal spectra. Physiological measurement. 2016; 37(9): 1556. doi: 10.1088/0967-3334/37/9/1556.
15. Kang S, Doroshow R, McConnaughey J, Shekhar R. Automated Identification of Innocent Still’s Murmur in Children. IEEE Transactions on Biomedical Engineering. 2017; 64(6): 1326-1334. doi: 10.1109/TBME.2016.2603787.
16. Zaitseva EG, Chernetsky MV, Shevel NA. About Possibility of Remote Diagnostics of the Respiratory System by Auscultation. Devices and Methods of Measurements. 2020; 11(2): 148-154. doi: 10.21122/2220-9506-2020-11-2-148-154.
17. Chamberlain D, Kodgule R, Ganelin D, Miglani V, Fletcher RR. Application of semi-supervised deep learning to lung sound analysis. 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. 2016: 804-807. doi: 10.1109/EMBC.2016.7590823.
18. Kim Y, Hyon Y, Jung SS, Lee S, Yoo G, Chung C, et al. Respiratory sound classification for crackles, wheezes, and rhonchi in the clinical field using deep learning. Scientific Reports. 2021; 11: 1-11. doi: 10.1038/s41598-021-96724-7.
19. Altan G, Kutlu Y, Pekmezci AÖ, Nural S. Deep learning with 3D-second order difference plot on respiratory sounds. Biomedical Signal Processing and Control. 2018; 45: 58-69. doi:10.1016/j.bspc.2018.05.014.
20. Berdibaeva G К , Bodin ON, Firsov DS. Classification of sounds of asthmatic breathing using neural networks. Measuring. Monitoring. Management. Control. 2018; 2(24): 86-90. ( In Russ. ). doi: 10.21685/2307-5538-2018-2-11.
21. Demir F, Sengur A, Bajaj V. Convolutional neural networks based efficient approach for classification of lung diseases. Health Information Science and Systems. 2020; 8(4): 1-8. doi: 10.1007/s13755-019-0091-3.
22. Naves R, Barbosa BH, Ferreira DD. Classification of lung sounds using higher-order statistics: A divide-and-conquer approach. Computer Methods and Programs in Biomedicine. 2016; 129: 12-20. doi: 10.1016/j.cmpb.2016.02.013.
23. Poreva AS, Vaityshyn VI, Karplyuk YeS. Machine learning methods for the study of the lungsounds signals. Microsystems, Electronics and Acoustics. 2017; 22(6): 41-47. ( In Russ. ). doi: 10.20535/2523-4455.2017.22.6.108829.
24. Bardou D, Zhang K, Ahmad SM. Lung sounds classification using convolutional neural networks. Artificial Intelligence in Medicine. 2018; 88: 58-69. doi: 10.1016/j.artmed.2018.04.008.
25. Aykanat M, Kılıç Ö, Kurt B, Saryal S. Classification of lung sounds using convolutional neural networks. Journal on Image and Video Processing. 2017; 1: 1-9. doi: 10.1186/s13640-017-0213-2.
26. Zhang J, Wang HS, Zhou HY, Dong B, Zhang L, Zhang F, et al. Real-World Verification of Artificial Intelligence Algorithm-Assisted Auscultation of Breath Sounds in Children. Frontiers in Pediatrics. 2021; 9: 152. doi : 10.3389/fped.2021.627337.
27. Grzywalski T, Piecuch M, Szajek M, Bręborowicz A, Hafke-Dys H, Kociński J, et al. Practical implementation of artificial intelligence algorithms in pulmonary auscultation examination. European Journal of Pediatrics. 2019; 178(6): 883-890. doi : 10.1007/s00431-019-03363-2.
28. Furman E, Charushin A, Eirikh E, Furman G, Sokolovsky V, Malinin S, et al. Capabilities of computer analysis of breath sounds in patients with COVID-19. Perm Medical Journal. 2021; 38(3): 97-109. ( In Russ. ). doi : 10.17816/pmj38397%109.
29. Lapteva EA, Kharevich ON, Khatsko VV, Voronova NA, Chamko MV, Bezruchko IV, et al. Automated lung sound analysis using the LungPass platform: A sensitive and specific tool for identifying lower respiratory tract involvement in COVID-19. European Respiratory Journal. 2021; 58(6): 2101907. doi : 10.1183/13993003.01907-2021.
30. Lapteva EA, Kovalenko IV, Laptev AN, Katibnikova EI, Pozdnyakova AS, Korovkin VS, et al. Application of the neural network technology for detection and monitoring of auscultative phenomena in diagnosis and treatment of diseases of the respiratory system. Journal of the Grodno State Medical University. 2020; 18(3): 230-235. ( In Russ. ). doi : 10.25298/2221-8785-2020-18-3-230-235.
For citation
Tutsenko K.O., Narkevich A.N., Rossiev D.A., Ipatyuk O.V., Avdeev S.M. Application of computer technologies using auscultation data for heart and lung diseases diagnosis. Medical doctor and information technology. 2022; 2: 12-21. (In Russ.). doi : 10.25881/18110193_2022_2_12.
Keywords