Perm Medical JournalPerm Medical JournalПермский медицинский журнал (сетевое издание "Perm medical journal")0136-14492687-1408Eco-Vector7601110.17816/pmj38397-109Methods of diagnostics and technologiesМетоды диагностики и технологииResearch ArticleCapabilities of computer analysis of breath sounds in patients with COVID-19Возможности компьютерного анализа дыхательных шумов у пациентов с заболеванием COVID-19FurmanE. G.ФурманЕ. Г.
Russian Federation

MD, PhD, Corresponding Member of RAS, Professor, Head of Department of Faculty and Hospital Pediatrics

член-корреспондент РАН, профессор, заведующий кафедрой факультетской и госпитальной педиатрии

furman1@yandex.ruCharushinA. O.ЧарушинА. О.
Russian Federation

Associate Professor, Department of Otorhinolaryngology

доцент кафедры оториноларингологии

furman1@yandex.ruEirikhE. S.ЭйрихЕ. С.
Russian Federation

Infectiologist

врач-инфекционист

furman1@yandex.ruFurmanG. B.ФурманГ. Б.
Israel

PhD, Professor, Department of Physics

PhD, профессор кафедры физики

furman1@yandex.ruSokolovskyV. L.СоколовскийВ. Л.
Israel

PhD, Professor, Department of Physics

PhD, профессор кафедры физики

furman1@yandex.ruMalininS. V.МалининС. В.
Russian Federation

Software Development Specialist

специалист по разработке программного обеспечения Центральной научно-исследовательской лаборатории

furman1@yandex.ruSheludkoV. S.ШелудькоВ. С.
Russian Federation

Candidate of Medical Sciences, Senior Researcher

кандидат медицинских наук, старший научный сотрудник микробиологической лаборатории Центральной научно-исследовательской лаборатории

furman1@yandex.ruPolyanskayaD. A.ПолянскаяД. А.
Russian Federation

Candidate of Medical Sciences, Lecture, Department Hygiene of Faculty of Preventive Medicine

кандидат медицинских наук, преподаватель кафедры гигиены медико-профилактического факультета

furman1@yandex.ruKalininaN. M.КалининаН. М.
Russian Federation

Forth-Year Student

студентка VI курса педиатрического факультета

furman1@yandex.ruShtivelmanD. K.ШтивельманД. К.
Israel

Lecturer, Faculty of Physics

преподаватель факультета физики

furman1@yandex.ruE.A. Vagner Perm State Medical UniversityПермский государственный медицинский университет имени академика Е.А. ВагнераPerm Regional Clinical Infectious HospitalПермская краевая клиническая инфекционная больницаBen-Gurion University of NegevУниверситет Бен-Гуриона в Негеве16072021383971091307202113072021Copyright ©; 2021, Furman E.G., Charushin A.O., Eirikh E.S., Furman G.B., Sokolovsky V.L., Malinin S.V., Sheludko V.S., Polyanskaya D.A., Kalinina N.M., Shtivelman D.K.Copyright ©; 2021, Фурман Е.Г., Чарушин А.О., Эйрих Е.С., Фурман Г.Б., Соколовский В.Л., Малинин С.В., Шелудько В.С., Полянская Д.А., Калинина Н.М., Штивельман Д.К.2021Furman E.G., Charushin A.O., Eirikh E.S., Furman G.B., Sokolovsky V.L., Malinin S.V., Sheludko V.S., Polyanskaya D.A., Kalinina N.M., Shtivelman D.K.Фурман Е.Г., Чарушин А.О., Эйрих Е.С., Фурман Г.Б., Соколовский В.Л., Малинин С.В., Шелудько В.С., Полянская Д.А., Калинина Н.М., Штивельман Д.К.http://creativecommons.org/licenses/by-sa/4.0https://permmedjournal.ru/PMJ/article/view/76011

Objective. To develop methods for a rapid distance computer diagnosis of COVID-19 based on the analysis of breath sounds. It is known that changes in breath sounds can be the indicators of respiratory organs diseases. Computer analysis of these sounds can indicate their typical changes caused by COVID-19, and can be used for a rapid preliminary diagnosis of this disease.

Materials and methods. The method of fast Fourier transform (FFT) was used for computer analysis of breath sounds, recorded near the mouth of 14 COVID-19 patients (aged 18–80 years) and 17 healthy volunteers (aged 5–48 years). The frequency of breath sound records ranged from 44 to 96 kHz. Unlike the conventional methods of computer analysis for diagnosis of diseases based on respiratory sound studying, we offer to test a high-frequency part of FFT (2000–6000 kHz).

Results. While comparing the breath sound FFT in patients and healthy volunteers, we developed the methods for COVID-19 computer diagnosis and determined the numerical criteria in patients and healthy persons. These criteria do not depend on sex and age of the examined persons.

Conclusions. The offered computer methods based on the analysis of breath sound FFT in patients and volunteers permit to diagnose COVID -19 with relatively high diagnostic parameters. These methods can be used in development of noninvasive means for preliminary self-express diagnosis of COVID-19.

Цель. Разработка методов быстрой дистанционной компьютерной диагностики COVID-19, основанных на анализе дыхательных шумов. Известно, что изменения дыхательных шумов могут являться индикатором заболеваний органов дыхания. Компьютерный анализ этих шумов может указывать на характерные их изменения, вызванные заболеванием COVID-19, и может быть использован для быстрой предварительной диагностики этого заболевания.

Материалы и методы. Метод быстрого преобразования Фурье (FFT) был применен для компьютерного анализа дыхательных шумов, записанных около рта 14 больных COVID-19 (возраст 18–80 лет) и 17 здоровых добровольцев (возраст от 5 до 48 лет). Частота записей дыхательных шумов была в диапазоне от 44 до 96 кГц. В отличие от обычных методов компьютерного анализа при диагностике заболеваний, основанных на анализе респираторного звука, мы предлагаем протестировать высокочастотную часть спектра FFT (2000–6000 Гц).

Результаты. Сравнивая спектры FFT дыхательных шумов пациентов и здоровых добровольцев, разработаны методы компьютерной диагностики COVID-19 и определены численные критерии у здоровых и больных. Эти критерии не зависят от пола и возраста обследованных.

Выводы. Предлагаемые компьютерные методы, основанные на анализе спектра FFT дыхательных шумов пациентов и добровольцев, позволяют диагностировать COVID-19 с достаточно высокими диагностическими показателями. Эти методы могут быть применены при разработке неинвазивных средств для самостоятельной предварительной экспресс-диагностики заболевания COVID-19.

COVID-19audio analysisdistant computer diagnosisbreath soundsCOVID-19аудиоанализудаленная компьютерная диагностикадыхательные звукиМинистерствo науки и технологий ИзраиляMinistry of Science and Technology of Israel3-16500Российский фонд фундаментальных исследований (РФФИ)Russian Foundation for Basic Research (RFBR)19-515-06001Worldometers, available at: https:// www.worldome¬ters.info/coronavirus/#countriesWorldometers, available at: https:// www.worldometers.info/coronavirus/#countriesPlotkin S.A. The New Coronavirus, the Current King of China. Journal of the Pediatric Infectious Diseases Society 2020; 9 (1): 1–2.Sant’Ambrogio G., Sant’Ambrogio F.B. Role of laryngeal afferents in cough. Pulm Pharmacol 1996; 9: 309–314.Stalin Raj, Huihui Mou, Saskia L., Smits et al. Dipeptidyl peptidase 4 is a functional receptor for the emerging human oronavirus-EMC. Nature 2013; 495 (7440): 251–254.Ke Wang, Wei Chen, Yu-Sen Zhou et al. SARS-CoV-2 invades host cells via a novel route: CD147-spike protein. bioRxiv 2020; 03: 14.Shuyi Yang, Yuxin Shi, Hongzhou Lu et al. Clinical and CT features of early-stage patients with COVID-19: a retrospective analysis of imported cases in Shanghai, China. Eur Respir J 2020; 55 (4): 2000407.Zhou P., Lou Y.X., Wang X.G., Hu B., Zhang L., Zhang W. A pneumonia outbreak associated with a new coronavirus of probable bat origin. Nature 2020; 579: 270–273.Ellington L.E., Gilman R.H., Tielsch J.M. et al. Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study. BMJ 2012; 2: e000506.Ellington L.E., Gilman R.H., Tielsch J.M. [et al.] 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 2012; 2: e000506.Marques A., Oliver A., Jacome C. Computerized adventitious respiratory sounds as outcome measures for respiratory therapy: a systematic review. Respiratory Care 2014; 59 (5): 765–776.Mhetre R., Bagal U.R. Respiratory sound analysis for diagnostic information. IOSR Journal of Electrical and Electronics Engineering (IOSR-JEEE) 2014; 9 (5): 42–46.Fenton T.R., Pasternakamp H., Tal A., Chernick V. Automated spectral characterization of wheezing in asthmatic children. IEEE Trans on Biomedical Engineering 1985; 1: 50–55.Reichert S., Gass R., Hajjam A. et. al The ASAP project: A first step to an auscultation’s school creation. Respiratory Medicine 2009; 2 (1): 7–14.Reichert S., Gass R., Hajjam A. et al. The ASAP project: A first step to an auscultation’s school creation. Respiratory Medicine 2009; 2 (1): 7–14.Wu Z., McGoogan J.M. Characteristics of and important lessons from the coronavirus disease 2019 (COVID-19) outbreak in China: summary of a report of 72314 cases from the Chinese Center for Disease Control and Prevention. JAMA 2020; 2: 24.Xaviero Adhi Pramono Renard, Bowyer Stuart, Rodriguez-Villegas Esther. Automatic adventitious respiratory sound analysis: A systematic review. PLOS ONE 2017; 12 (5): e0177926.Olvera-Montes N., Reyes B., Charleston-Villalobos S. et al. Detection of Respiratory Crackle Sounds via an Android Smartphone-based System. Conf Proc IEEE Eng Med Biol Soc 2018; 2018: 1620–1623.Reyes B.A., Olvera-Montes N., Charleston-Villalobos S., González-Camarena R., Mejía-Ávila M., Aljama-Corrales T. A Smartphone-Based System for Automated Bedside Detection of Crackle Sounds in Diffuse Interstitial Pneumonia Patients. Sensors 2018; 18: 3813.Björn W. Schuller, Dagmar M. Schuller, Kun Qian et al. COVID-19 and computer audition: An overview on what speech & sound analysis could contribute in the SARS-CoV-2 Corona crisis. arXiv 2020; 2003: 11117.Schuller Björn W., Schuller Dagmar M., Kun Qian et al. COVID-19 and computer audition: An overview on what speech & sound analysis could contribute in the SARS-CoV-2 Corona crisis. arXiv. 2020; preprint arXiv: 2003: 11117.Faezipour M., Abuzneid A. Smartphone-Based Self-Testing of COVID-19 Using Breathing Sounds. Telemedicine and e-Health 2020.Chloë Brown, Jagmohan Chauhan, Andreas Grammenos et al. Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. arXiv 2020; 2006: 05919.Chloë Brown, Jagmohan Chauhan, Andreas Grammenos et al. Exploring Automatic Diagnosis of COVID-19 from Crowdsourced Respiratory Sound Data. arXiv preprint arXiv, 2006: 05919.Furman E., Yakovleva E., Malinin S., Furman G., Sokolovsky V. Computer-assisted assay of respiratory sound of children suffering from bronchial asthma. Clinical Medicine 2014; 6 (1): 83–87.Furman E. A new modality using breath sound analysis in pediatric asthma. Clinical and Translational Allergy 2014; 4: 105.Furman E.G., Rocheva E.V., Malinin S.V., Furman G.B., Sokolovsky V.L. Comparative effectiveness of computer analysis of the energy characteristics of the respiratory noise spectrum at three points for the diagnosis of bronchial obstructive syndrome in children with bronchial asthma]. Permskii meditsinskii zhurnal 2015; 32: 77–88.Furman E.G., Sokolovsky V.L., Furman G.B., Meerovich V.M., Malinin S.V., Rocheva E.V. Mathematical model of breath sound propagation in respiratory tract. Russian Journal of Biomechanics 2018; 22 (2): 142–152Malinin S., Furman E., Rocheva E., Sokolovsky V., Furman G. The home remote diagnostics of bronchial asthma in children with the using of telemedical system. ERS International Congress 2019.Furman E., Malinin S., Furman G., Meerovich V., Sokolovsky V., Rocheva E. Respiratory Sound Analysis for Bronchial Asthma Diagnostics. IOSR Journal of Engineering (IOSRJEN) 2020; 10 (1): 53–59.Furman E., Malinin S., Furman G., Meerovich V., Sokolovsky V., Rocheva E. Respiratory Sound Analysis for Bronchial Asthma Diagnostics. IOSR Journal of Engineering (IOSRJEN). 2020; 10 (1): 53–59.Reddel H.K., FitzGerald J.M., Bateman E.D. et al. GINA 2019: a fundamental change in asthma management. Eur Respir J 2019; 53: 1901046Reddel H.K., FitzGerald J.M., Bateman E.D. et al. GINA 2019: a fundamental change in asthma management. Eur Respir J 2019; 53: 1901046.Sovijärvi A.R.A., Dalmasso F., Vanderschoot J., Malmberg L.P., Righini G., S.A.T. Stoneman Definition of terms for applications of respiratory sounds. European Respiratory Review 2000; 10 (77): 597–610.Sovijärvi A.R.A., Dalmasso F., Vander¬schoot J., Malmberg L.P., Righini G., S.A.T. Stoneman Definition of terms for applications of respiratory sounds. European Respiratory Review 2000; 10 (77): 597–610.Kosasih K., Abeyratne U.R., Swarnkar V. High frequency analysis of cough sounds in pediatric patients with respiratory diseases. 2012 Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE 2012: 5654–5657.Taplidou S.A., Hadjileontiadis L.J., Kitsas I.K. et al. On Applying Continuous Wavelet Transform in Wheeze Analysis," The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco CA 2004; 3832–3835.Taplidou S.A., Hadjileontiadis L.J., Kitsas I.K. et al. On Applying Continuous Wavelet Transform in Wheeze Analysis. The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society. San Francisco CA 2004; 3832–3835.