Аускультация является типовым методом обследования пациентов с патологиями органов дыхания и сердечно-сосудистой системы. Это дешевый и доступный, но субъективный метод, диагностическая ценность которого сильно зависит от опыта врача. Электронные стетоскопы способны увеличивать громкость аудиозаписи, устранять шумы, а также хранить и передавать звук на компьютер или смартфон. Для фильтрации полученных аудиозаписей используется вейвлет-преобразование, фильтр Баттерворта, фильтры нижних и верхних частот и другие. Для идентификации звуков используются методы машинного обучения, которые зачастую превосходят в точности опытных врачей. Методы математического анализа позволяют диагностировать патологические и невинные сердечные шумы, хрипы в лёгких, астматическое дыхание и другие патологии. В данном обзоре описываются различные исследования, посвященные диагностике патологий органов дыхания и сердечно-сосудистой системы по данным аускультации.
Литература
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/s0929-693x(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. Бердибаева Г.К., Бодин О.Н., Фирсов Д.С. Классификация звуков астматического дыхания с использованием нейронных сетей // Измерение. Мониторинг. Управление. Контроль. — 2018. — №2(24). — С.86-90. [Berdibaeva GК, 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. Порева А.С., Вайтышин В.И., Карплюк Е.С. Методы машинного обучения для исследования звуков легких // Мікросистеми, Електроніка та Акустика. — 2017. — Т.22. — №6. — С.41-47. 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. Фурман Е.Г., Чарушин А., Эйрих Е., Фурман Г., Соколовский В., Малинин C. и др. Возможности компьютерного анализа дыхательных шумов у пациентов с заболеванием COVID-19 // Пермский медицинский журнал. — 2021. — Т.38. — №3. — C.97-109. 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. Лаптева Е.А., Коваленко И.В., Лаптев А.Н., Катибникова Е.И., Позднякова А.С., Коровкин В.С. и др. Применение технологии «нейронных сетей» для выявления и мониторинга аускультативных феноменов при диагностике заболеваний органов дыхания // Журнал Гродненского государственного медицинского университета. — 2020. — Т.18. — №3. — С.230-235. 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/s0929-693x(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. Бердибаева Г.К., Бодин О.Н., Фирсов Д.С. Классификация звуков астматического дыхания с использованием нейронных сетей // Измерение. Мониторинг. Управление. Контроль. — 2018. — №2(24). — С.86-90. [Berdibaeva GК, 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. Порева А.С., Вайтышин В.И., Карплюк Е.С. Методы машинного обучения для исследования звуков легких // Мікросистеми, Електроніка та Акустика. — 2017. — Т.22. — №6. — С.41-47. 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. Фурман Е.Г., Чарушин А., Эйрих Е., Фурман Г., Соколовский В., Малинин C. и др. Возможности компьютерного анализа дыхательных шумов у пациентов с заболеванием COVID-19 // Пермский медицинский журнал. — 2021. — Т.38. — №3. — C.97-109. 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. Лаптева Е.А., Коваленко И.В., Лаптев А.Н., Катибникова Е.И., Позднякова А.С., Коровкин В.С. и др. Применение технологии «нейронных сетей» для выявления и мониторинга аускультативных феноменов при диагностике заболеваний органов дыхания // Журнал Гродненского государственного медицинского университета. — 2020. — Т.18. — №3. — С.230-235. doi: 10.25298/2221-8785-2020-18-3-230-235.
Для цитирования
Туценко К.О., Наркевич А.Н., Россиев Д.А., Ипатюк О.В., Авдеев С.М. Применение компьютерных технологий для диагностики заболеваний сердца и лёгких по данным аускультации. Врач и информационные технологии. 2022; 2: 12-21. doi: 10.25881/18110193_2022_2_12.
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