cfpdБюллетень физиологии и патологии дыханияBulletin Physiology and Pathology of Respiration1998-5029Дальневосточный научный центр физиологии и патологии дыхания10.36604/1998-5029-2023-89-159-173cfpd-1127Research ArticleОБЗОРЫREVIEWSКонтактные способы регистрации частоты дыхания: возможности и перспективыContact methods for registering respiratory rate: opportunities and perspectivesГаранинА. А.GaraninA. A.

Андрей Александрович Гаранин, кандидат медицинских наук, директор научно-практического центра дистанционной медицины Клиник

443099, г. Самара, ул. Чапаевская, 89

Andrey A. Garanin, MD, PhD (Med.), Director of Scientific and Practical Centre of Distant Medicine, Clinics 

89 Chapaevskaya Str., Samara, 443099

a.a.garanin@samsmu.ruРубаненкоА. О.RubanenkoA. O.

Анатолий Олегович Рубаненко, кандидат медицинских наук, доцент кафедры пропедевтической терапии 

443099, г. Самара, ул. Чапаевская, 89

Anatoliy O. Rubanenko, MD, PhD (Med.), Associate Professor of Propaedeutic Therapy Department

89 Chapaevskaya Str., Samara, 443099

a.o.rubanenko@samsmu.ruШипуновИ. Д.ShipunovI. D.

Иван Дмитриевич Шипунов, врач по медицинской профилактике научно-практического центра дистанционной медицины Клиник

443099, г. Самара, ул. Чапаевская, 89

Ivan D. Shipunov, MD, Doctor of Medical Prevention, Scientific and Practical Centre of Distant Medicine, Clinics 

89 Chapaevskaya Str., Samara, 443099

i.d.shipunov@samsmu.ruРоговаВ. С.RogovaV. S.

Валерия Сергеевна Рогова, врач по медицинской профилактике научно-практического центра дистанционной медицины Клиник

443099, г. Самара, ул. Чапаевская, 89

Valeriya S. Rogova, MD, Doctor of Medical Prevention, Scientific and Practical Centre of Distant Medicine, Clinics 

89 Chapaevskaya Str., Samara, 443099

v.s.rogova@samsmu.ruФедеральное государственное бюджетное образовательное учреждение высшего образования «Самарский государственный медицинский университет» Министерства здравоохранения Российской ФедерацииSamara State Medical University202301102023089159173Copyright © Гаранин А.А., Рубаненко А.О., Шипунов И.Д., Рогова В.С., 20232023Гаранин А.А., Рубаненко А.О., Шипунов И.Д., Рогова В.С.Garanin A.A., Rubanenko A.O., Shipunov I.D., Rogova V.S.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://cfpd.elpub.ru/jour/article/view/1127Введение

Введение. Частота дыхания, как известно, является одним из наиболее важных показателей, отражающих жизненно важные функции человека. Увеличение частоты дыхания может наблюдаться при многих заболеваниях и синдромах, например, при хронической обструктивной болезни легких, пневмонии, бронхиальной астме, инфаркте миокарда, сердечной недостаточности, анемии и т.д. В связи с активным внедрением в клиническую практику телемедицинского мониторинга, измерение вышеуказанного показателя представляется особенно актуальным с целью раннего выявления и профилактики осложнений хронических неинфекционных заболеваний, а также проведения динамического контроля состояния пациентов, как в стационарных, так и в амбулаторных условиях.

Цель

Цель. Поиск и актуализация сведений о существующих и перспективных разработках для контроля частоты дыхания, основанных на разных физических принципах. Поиск информации осуществлялся в поисковых системах и наукометрических базах PubMed, Scopus, MedLine и РИНЦ. Для поиска использовались следующие ключевые слова: «respiratory rate», «contact», «measurement», «sensor».

Результаты

Результаты. Контактные методы определения частоты дыхания включают в себя широкую номенклатуру датчиков, основанных на различных физических принципах. Все разновидности датчиков имеют свои сферы применения, однако при этом не лишены недостатков. Для достижения максимальной точности мониторинга частоты дыхания необходимо тщательно оценивать условия, в которых находится пациент, подбирая к ним наиболее подходящие технологические решения. Вероятно, комплексные системы, включающие в себя несколько различных датчиков, способны нивелировать многие недостатки. Кроме того, развитие методов информационного анализа, технологии машинного обучения и искусственного интеллекта способны увеличить чувствительность и точность методов, снижая частоту ошибок, связанных с различными искажениями и артефактами.

Заключение

Заключение. Таким образом, технологическое развитие открывает широкие возможности для длительного мониторинга витальных функций, профилактики и своевременного реагирования на неблагоприятные события.

Introduction

Introduction. Respiratory rate is known to be one of the most important indicators reflecting the vital functions of a person. An increase in respiratory rate can be found in many diseases and pathological conditions, for example, in chronic obstructive pulmonary disease, pneumonia, bronchial asthma, myocardial infarction, heart failure, anaemia, etc. Due to the active introduction of telemedicine monitoring into clinical practice, the measurement of the abovementioned indicator is particularly relevant for the purpose of early detection and prevention of complications of chronic non-infectious diseases, as well as dynamic monitoring of the condition of patients in both inpatient and outpatient settings.

Aim

Aim. To search and update information about existing and promising developments for the control of respiratory rate based on different physical principles.

Materials and methods

Materials and methods. For this review we used databases PubMed, Scopus, MedLine and eLIBRARY. The following keywords were used for the search: “respiratory rate”, “contact”, “measurement”, “sensor”.

Results

Results. Contact methods for measuring respiratory rate include a wide range of sensors based on various physical principles. All types of sensors have their own application, but also they have some drawbacks. In order to achieve maximum accuracy of respiratory rate monitoring, it is necessary to carefully assess the conditions in which the patient is located, selecting the most appropriate technological solutions for them. Probably, complex systems, including several different sensors, are able to overcome many shortcomings. In addition, the development of information analysis methods, machine learning and artificial intelligence technologies can increase the sensitivity and accuracy of methods of measuring respiratory rate, reducing the frequency of bias associated with various artefacts.

Conclusion

Conclusion. Thus, technological development opens up wide opportunities for long-term monitoring of vital functions, prevention and timely response to adverse events.

частота дыханияконтактные датчикимониторингизмерениеrespiratory ratecontact sensorsmonitoringmeasurementReferencesFolke M., Cernerud L., Ekström M., Hök B. Critical review of non-invasive respiratory monitoring in medical care // Med. Biol. Eng. Comput. 2003. Vol.41, Iss.4. P.377–383. https://doi.org/10.1007/BF02348078Folke M., Cernerud L., Ekström M., Hök B. Critical review of non-invasive respiratory monitoring in medical care. Med. Biol. Eng. Comput. 2003; 41(4):377–383. https://doi.org/10.1007/BF02348078AL-Khalidi F. Q., Saatchi R., Burke D., Elphick H., Tan S. Respiration rate monitoring methods: A review // Pediatr. Pulmonol. 2011. Vol.46, Iss.6. P.523–529. https://doi.org/10.1002/ppul.21416AL-Khalidi F. Q., Saatchi R., Burke D., Elphick H., Tan S. Respiration rate monitoring methods: A review. Pediatr. Pulmonol. 2011; 46(6):523–529. https://doi.org/10.1002/ppul.21416Schena E., Massaroni C., Saccomandi P., Cecchini S. Flow measurement in mechanical ventilation: a review // Med. Eng. Phys. 2015. Vol.37, Iss.3. P.257–264. https://doi.org/10.1016/j.medengphy.2015.01.010Schena E., Massaroni C., Saccomandi P., Cecchini S. Flow measurement in mechanical ventilation: a review. Med. Eng. Phys. 2015; 37(3):257–264. https://doi.org/10.1016/j.medengphy.2015.01.010Stocks J., Sly P.D., Tepper R.S., Morgan W.J. Infant Respiratory Function Testing. John Wiley & Sons: Hoboken. NJ. USA, 1996. 674 p.Stocks J., Sly P.D., Tepper R.S., Morgan W.J. Infant Respiratory Function Testing. John Wiley & Sons: Hoboken. NJ. USA; 1996.Lilly J.C. Flow meter for recording respiratory flow of human subjects // Methods Med. Res. 1950. Vol.2. P.113– 121.Lilly J.C. Flow meter for recording respiratory flow of human subjects. Methods Med. Res. 1950; 2:113–121.Tardi G., Massaroni C., Saccomandi P., Schena E. Experimental assessment of a variable orifice flowmeter for respiratory monitoring // J. Sens. 2015. Vol.7. P.1–7. https://doi.org/10.1155/2015/752540Tardi G., Massaroni C., Saccomandi P., Schena E. Experimental assessment of a variable orifice flowmeter for respiratory monitoring. J. Sens. 2015; 7:1–7. https://doi.org/10.1155/2015/752540Schena E., Saccomandi P., Silvestri S. A high sensitivity fiber optic macro-bend based gas flow rate transducer for low flow rates: theory, working principle, and static calibration // Rev. Sci. Instrum. 2013. Vol.84, Iss.2. Article number: 024301. https://doi.org/10.1063/1.4793227Schena E., Saccomandi P., Silvestri S. A high sensitivity fiber optic macro-bend based gas flow rate transducer for low flow rates: theory, working principle, and static calibration. Rev. Sci. Instrum. 2013; 84(2):024301. https://doi.org/10.1063/1.4793227Hoppe P. Temperatures of expired air under varying climatic conditions // Int. J. Biometeorol. 1981. Iss.25. P.127– 132. https://doi.org/10.1007/BF02184460Hoppe P. Temperatures of expired air under varying climatic conditions. Int. J. Biometeorol. 1981; 25:127–132. https://doi.org/10.1007/BF02184460Suzuki S., Matsui T., Kawahara H., Ichiki H., Shimizu J., Kondo Y., Gotoh S., Yura H., Takase B., Ishihara M. A non-contact vital sign monitoring system for ambulances using dual-frequency microwave radars // Med. Biol. Eng. Comput. 2009. Vol.47, Iss.1. P.101–105. https://doi.org/10.1007/s11517-008-0408-xSuzuki S., Matsui T., Kawahara H., Ichiki H., Shimizu J., Kondo Y., Gotoh S., Yura H., Takase B., Ishihara M. A non-contact vital sign monitoring system for ambulances using dual-frequency microwave radars. Med. Biol. Eng. Comput. 2009; 47(1):101–105. https://doi.org/10.1007/s11517-008-0408-xStorck K., Karlsson M., Ask P., Loyd D. Heat transfer evaluation of the nasal thermistor technique // IEEE Trans Biomed Eng. 1996. Vol.43, Iss.12. P.1187–1191. https://doi.org/10.1109/10.544342Storck K., Karlsson M., Ask P., Loyd D. Heat transfer evaluation of the nasal thermistor technique. IEEE Trans Biomed Eng. 1996; 43(12):1187–1191. https://doi.org/10.1109/10.544342Lim S., Park S.H., Ahn S.D., Suh Y., Shin S.S., Lee S.W., Kim J.H., Choi E.K., Yi B.Y., Kwon S.I., Kim S., Jeung T.S. Guiding curve based on the normal breathing as monitored by thermocouple for regular breathing // Med. Phys. 2007. Vol.34, Iss.11. P.4514–4518. https://doi.org/10.1118/1.2795829Lim S., Park S.H., Ahn S.D., Suh Y., Shin S.S., Lee S.W., Kim J.H., Choi E.K., Yi B.Y., Kwon S.I., Kim S., Jeung T.S. Guiding curve based on the normal breathing as monitored by thermocouple for regular breathing. Med. Phys. 2007; 34(11):4514–4518. https://doi.org/10.1118/1.2795829Krehel M., Schmid M., Rossi R.M., Boesel L.F., Bona G.L., Scherer L.J. An optical fibre-based sensor for respiratory monitoring // Sensors (Basel). 2014. Vol.14, Iss.7. P.13088–131101. https://doi.org/10.3390/s140713088Krehel M., Schmid M., Rossi R.M., Boesel L.F., Bona G.L., Scherer L.J. An optical fibre-based sensor for respiratory monitoring. Sensors (Basel) 2014; 14(7):13088–131101. https://doi.org/10.3390/s140713088Branson R.D., Gentile M.A. Is humidification always necessary during noninvasive ventilation in the hospital? // Respir. Care. 2010. Vol.55, Iss.2. P.209–216. PMID: 20105346.Branson R.D., Gentile M.A. Is humidification always necessary during noninvasive ventilation in the hospital? Respir. Care 2010; 55(2):209–216. PMID: 20105346.Farahani H., Wagiran R., Hamidon M.N. Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review // Sensors. 2014. Vol.14, Iss.5. P.7881–7939. https://doi.org/10.3390/s140507881Farahani H., Wagiran R., Hamidon M.N. Humidity Sensors Principle, Mechanism, and Fabrication Technologies: A Comprehensive Review. Sensors 2014; 14(5):7881–7939. https://doi.org/10.3390/s140507881Kano S., Kim K., Fujii M. Fast-Response and Flexible Nanocrystal-Based Humidity Sensor for Monitoring Human Respiration and Water Evaporation on Skin // ACS Sens. 2017. Vol.2, Iss.6. P.828–833. https://doi.org/10.1021/acssensors.7b00199Kano S., Kim K., Fujii M. Fast-Response and Flexible Nanocrystal-Based Humidity Sensor for Monitoring Human Respiration and Water Evaporation on Skin. ACS Sens. 2017; 2(6):828–833. https://doi.org/10.1021/acssensors.7b00199Kano S., Dobashi Y., Fujii M. Silica Nanoparticle-Based Portable Respiration Sensor for Analysis of Respiration Rate, Pattern, and Phase During Exercise // IEEE Sensors Letters. Vol.2, Iss.1. P.1–4. https://doi.org/10.1109/LSENS.2017.2787099Kano S., Dobashi Y., Fujii M. Silica Nanoparticle-Based Portable Respiration Sensor for Analysis of Respiration Rate, Pattern, and Phase During Exercise. IEEE Sensors Letters. 2017; 2(1):1–4. https://doi.org/10.1109/LSENS.2017.2787099Зарецкий А.П., Митягин К.С., Тарасов В.С., Мороз Д.Н. Оценка параметров дыхательной активности пациента на основе данных фотоплетизмографии // Труды Московского физико-технического института (национального исследовательского университета). 2019. Т.11, №3(43). С.61–69. EDN: FRSGND.Zaretskiy A.P., Mityagin K.S., Tarasov V.S., Moroz D.N. [The respiratory rate estimation for a patient based on photoplethysmography data]. Trudy Moskovskogo fiziko-tekhnicheskogo instituta (natsional'nogo issledovatel'skogo universiteta) = Proceedings of Moscow Institute of Physics and Technology 2019; 11(3):61–69 (in Russian).Seifi S., Khatony A., Moradi G., Abdi A., Najafi F. Accuracy of pulse oximetry in detection of oxygen saturation in patients admitted to the intensive care unit of heart surgery: comparison of finger, toe, forehead and earlobe probes // BMC Nurs. 2018. Vol.17. Article number: 15. https://doi.org/10.1186/s12912-018-0283-1Seifi S., Khatony A., Moradi G., Abdi A., Najafi F. Accuracy of pulse oximetry in detection of oxygen saturation in patients admitted to the intensive care unit of heart surgery: comparison of finger, toe, forehead and earlobe probes. BMC Nurs. 2018; 17:15. https://doi.org/10.1186/s12912-018-0283-1Castaneda D., Esparza A., Ghamari M., Soltanpur C., Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care // Int. J. Biosens. Bioelectron. 2018. Vol.4, Iss.4. P.195–202. https://doi.org/10.15406/ijbsbe.2018.04.00125Castaneda D., Esparza A., Ghamari M., Soltanpur C., Nazeran H. A review on wearable photoplethysmography sensors and their potential future applications in health care. Int. J. Biosens. Bioelectron. 2018; 4(4):195–202. https://doi.org/10.15406/ijbsbe.2018.04.00125García-López I., Pramono R.X.A., Rodriguez-Villegas E. Artifacts classification and apnea events detection in neck photoplethysmography signals // Med. Biol. Eng. Comput. 2022. Iss.60. P.3539–3554. https://doi.org/10.1007/s11517-022-02666-1García-López I., Pramono R.X.A., Rodriguez-Villegas E. Artifacts classification and apnea events detection in neck photoplethysmography signals. Med Biol Eng Comput. 2022; 60:3539–3554. https://doi.org/10.1007/s11517-022-02666-1Mingxu P., Imtiaz S.A., Rodriguez-Villegas E. Pulse oximetry in the neck - a proof of concept // Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2017. P.877–880. https://doi.org/10.1109/EMBC.2017.8036964Mingxu P., Imtiaz S.A., Rodriguez-Villegas E. Pulse oximetry in the neck - a proof of concept. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2017:877–880. https://doi.org/10.1109/EMBC.2017.8036964Garcia-Lopez I., Imtiaz S.A., Rodriguez-Villegas E. Characterization Study of Neck Photoplethysmography // Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018. P.4355−4358. https://doi.org/10.1109/EMBC.2018.8513247Garcia-Lopez I., Imtiaz S.A., Rodriguez-Villegas E. Characterization Study of Neck Photoplethysmography. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2018:4355–4358. https://doi.org/10.1109/EMBC.2018.8513247Rogers B., Schaffarczyk M., Gronwald T. Estimation of Respiratory Frequency in Women and Men by Kubios HRV Software Using the Polar H10 or Movesense Medical ECG Sensor during an Exercise Ramp // Sensors (Basel). 2022. Vol.22, Iss.19. Article number: 7156. https://doi.org/10.3390/s22197156.Rogers B., Schaffarczyk M., Gronwald T. Estimation of Respiratory Frequency in Women and Men by Kubios HRV Software Using the Polar H10 or Movesense Medical ECG Sensor during an Exercise Ramp. Sensors (Basel) 2022; 22(19):7156. https://doi.org/10.3390/s22197156Berntson G.G., Cacioppo J.T., Quigley K.S. Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications // Psychophysiology. 1993. Vol.30, Iss.2. P.183−196. https://doi.org/10.1111/j.1469-8986.1993.tb01731.xBerntson G.G., Cacioppo J.T., Quigley K.S. Respiratory sinus arrhythmia: autonomic origins, physiological mechanisms, and psychophysiological implications. Psychophysiology 1993; 30(2):183–196. https://doi.org/10.1111/j.1469-8986.1993.tb01731.xHelfenbein E., Firoozabadi R., Chien S., Carlson E., Babaeizadeh S. Development of three methods for extracting respiration from the surface ECG: a review // J. Electrocardiol. 2014. Vol.47, Iss.6. P.819−825. https://doi.org/10.1016/j.jelectrocard.2014.07.020Helfenbein E., Firoozabadi R., Chien S., Carlson E., Babaeizadeh S. Development of three methods for extracting respiration from the surface ECG: a review. J. Electrocardiol. 2014; 47(6):819−825. https://doi.org/10.1016/j.jelectrocard.2014.07.020Chi Y.M., Jung T.P., Cauwenberghs G. Dry-contact and noncontact biopotential electrodes: methodological review // IEEE Rev. Biomed. Eng. 2010. Vol.3. P.106−119. https://doi.org/10.1109/RBME.2010.2084078Chi Y.M., Jung T.P., Cauwenberghs G. Dry-contact and noncontact biopotential electrodes: methodological review. IEEE Rev. Biomed. Eng. 2010; 3:106–119. https://doi.org/10.1109/RBME.2010.2084078Alikhani I., Noponen K., Hautala A., Ammann R., Seppänen T. Spectral fusion-based breathing frequency estimation; experiment on activities of daily living // BioMed. Eng. OnLine. 2018. Vol.17, Iss.1. Article number: 99. https://doi.org/10.1186/s12938-018-0533-1Alikhani I., Noponen K., Hautala A., Ammann R., Seppänen T. Spectral fusion-based breathing frequency estimation; experiment on activities of daily living. BioMed. Eng. OnLine. 2018; 17(1):99. https://doi.org/10.1186/s12938-018-0533-1Andreozzi E., Centracchio J., Punzo V., Esposito D., Polley C., Gargiulo G.D., Bifulco P. Respiration Monitoring via Forcecardiography Sensors // Sensors (Basel). 2021. Vol.21, Iss.12. Article number: 3996. https://doi.org/10.3390/s21123996Andreozzi E., Centracchio J., Punzo V., Esposito D., Polley C., Gargiulo G.D., Bifulco P. Respiration Monitoring via Forcecardiography Sensors. Sensors (Basel) 2021; 21(12):3996. https://doi.org/10.3390/s21123996Lu X., Azevedo Coste C., Nierat M.-C., Renaux S., Similowski T., Guiraud D. Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration // Sensors (Basel). 2021. Vol.21. Article number: 99. https://doi.org/10.3390/s21010099Lu X., Azevedo Coste C., Nierat M.-C., Renaux S., Similowski T., Guiraud D. Respiratory Monitoring Based on Tracheal Sounds: Continuous Time-Frequency Processing of the Phonospirogram Combined with Phonocardiogram-Derived Respiration. Sensors (Basel) 2021; 21:99. https://doi.org/10.3390/s21010099Eisenberg M.E., Givony D., Levin R. Acoustic respiration rate and pulse oximetry-derived respiration rate: a clinical comparison study // J. Clin. Monit. Comput. 2020. Vol.34. P.139–146. https://doi.org/10.1007/s10877-018-0222-4Eisenberg M.E., Givony D., Levin R. Acoustic respiration rate and pulse oximetry-derived respiration rate: a clinical comparison study. J. Clin. Monit. Comput. 2020; 34:139–146. https://doi.org/10.1007/s10877-018-0222-4Дацок О.М., Витанова С.А. Обработка фонокардиографического сигнала на основе wavelet технологий // Вестник Национального технического университета Харьковский политехнический институт. Серия: Информатика и моделирование. 2008. №24. С.36–41. EDN: RUOLFH.Datsok O.M., Vitanova S.A. [Processing a phonocardiographical signal on the basis of wavelet technologies]. Herald of the National Technical University "KhPI". Subject issue: Information Science and Modelling 2008; (24):36−41 (in Russian).Abbasi-Kesbi R., Valipour A., Imani K. Cardiorespiratory system monitoring using a developed acoustic sensor // Healthcare Technol. Lett. 2018. Vol.5, Iss.1. P.7−12. https://doi.org/10.1049/htl.2017.0012Abbasi-Kesbi R., Valipour A., Imani K. Cardiorespiratory system monitoring using a developed acoustic sensor. Healthcare Technol. Lett. 2018; 5(1):7–12. https://doi.org/10.1049/htl.2017.0012Janssens J.P., Cantero C., Pasquina P., Georges M., Rabec C. Monitoring Long Term Noninvasive Ventilation: Benefits, Caveats and Perspectives // Front. Med. (Lausanne). 2022. Vol.9. Article number: 874523. https://doi.org/10.3389/fmed.2022.874523Janssens J.P., Cantero C., Pasquina P., Georges M., Rabec C. Monitoring Long Term Noninvasive Ventilation: Benefits, Caveats and Perspectives. Front. Med. (Lausanne). 2022; 9:874523. https://doi.org/10.3389/fmed.2022.874523Ando H., Ashcroft-Kelso H., Halhead R., Chakrabarti B., Young C. A., Cousins R., Angus R. M. Experience of telehealth in people with motor neurone disease using noninvasive ventilation // Disabil. Rehabil. Assist. Technol. 2019. Vol.16, Iss.5. P.490–496. https://doi.org/10.1080/17483107.2019.1659864Ando H., Ashcroft-Kelso H., Halhead R., Chakrabarti B., Young C. A., Cousins R., Angus R. M. Experience of telehealth in people with motor neurone disease using noninvasive ventilation. Disabil. Rehabil. Assist. Technol. 2019; 16(5):490–496. https://doi.org/10.1080/17483107.2019.1659864Janssens J.P., Borel J.C., Pépin J.L., groupe SomnoVNI. Nocturnal monitoring of home non-invasive ventilation: the contribution of simple tools such as pulse oximetry, capnography, built-in ventilator software and autonomic markers of sleep fragmentation // Thorax. 2011. Vol.66, Iss.5. P.438−445. https://doi.org/10.1136/thx.2010.139782Janssens J.P., Borel J.C., Pépin J.L., groupe SomnoVNI. Nocturnal monitoring of home non-invasive ventilation: the contribution of simple tools such as pulse oximetry, capnography, built-in ventilator software and autonomic markers of sleep fragmentation. Thorax 2011; 66(5):438−445. https://doi.org/10.1136/thx.2010.139782Bergese S.D., Mestek M.L., Kelley S.D., McIntyre R. Jr., Uribe A.A., Sethi R., Watson J.N., Addison P.S. Multicenter Study Validating Accuracy of a Continuous Respiratory Rate Measurement Derived From Pulse Oximetry: A Comparison With Capnography // Anesth. Analg. 2017. Vol.124, Iss.4. P.1153−1159. https://doi.org/10.1213/ANE.0000000000001852Bergese S.D., Mestek M.L., Kelley S.D., McIntyre R. Jr., Uribe A.A., Sethi R., Watson J.N., Addison P.S. Multicenter Study Validating Accuracy of a Continuous Respiratory Rate Measurement Derived From Pulse Oximetry: A Comparison With Capnography. Anesth. Analg. 2017; 124(4):1153−1159. https://doi.org/10.1213/ANE.0000000000001852Hutchison R., Rodriguez L. Capnography and Respiratory Depression // AJN, American Journal of Nursing. 2008. Vol.108, Iss.2. P.35–39. https://doi.org/10.1097/01.naj.0000310329.55432.9fHutchison R., Rodriguez L. Capnography and Respiratory Depression. AJN, American Journal of Nursing 2008; 108(2):35–39. https://doi.org/10.1097/01.naj.0000310329.55432.9fKhanna A.K, Bergese S.D, Jungquist C.R, Morimatsu H., Uezono S., Lee S., Ti L.K., Urman R.D., McIntyre R. Jr., Tornero C., Dahan A., Saager L., Weingarten T.N., Wittmann M., Auckley D., Brazzi L., Le Guen M., Soto R., Schramm F., Ayad S., Kaw R., Di Stefano P., Sessler D.I., Uribe A., Moll V., Dempsey S.J., Buhre W., Overdyk F.J. Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial // Anesth. Analg. 2020. Vol.131, Iss.4. P.1012−1024. https://doi.org/10.1213/ANE.0000000000004788Khanna A.K, Bergese S.D, Jungquist C.R, Morimatsu H., Uezono S., Lee S., Ti L.K., Urman R.D., McIntyre R. Jr., Tornero C., Dahan A., Saager L., Weingarten T.N., Wittmann M., Auckley D., Brazzi L., Le Guen M., Soto R., Schramm F., Ayad S., Kaw R., Di Stefano P., Sessler D.I., Uribe A., Moll V., Dempsey S.J., Buhre W., Overdyk F.J. Prediction of Opioid-Induced Respiratory Depression on Inpatient Wards Using Continuous Capnography and Oximetry: An International Prospective, Observational Trial. Anesth. Analg. 2020; 131(4):1012−1024. https://doi.org/10.1213/ANE.0000000000004788Hamdani S.T.A., Fernando A. The Application of a Piezo-Resistive Cardiorespiratory Sensor System in an Automobile Safety Belt // Sensors. 2015. Vol.15, Iss.4. P.7742−7753. https://doi.org/10.3390/s150407742Hamdani S.T.A., Fernando A. The Application of a Piezo-Resistive Cardiorespiratory Sensor System in an Automobile Safety Belt. Sensors 2015; 15(4):7742−7753. https://doi.org/10.3390/s150407742Atalay O., Kennon W.R., Demirok E. Weft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its ElectroMechanical Modeling // IEEE Sensors Journal. 2015. Vol.15, Iss.1. P.110−112. https://doi.org/10.1109/JSEN.2014.2339739Atalay O., Kennon W.R., Demirok E. Weft-Knitted Strain Sensor for Monitoring Respiratory Rate and Its ElectroMechanical Modeling. IEEE Sensors Journal 2015; 15(1):110−112. https://doi.org/10.1109/JSEN.2014.2339739Paradiso R., Loriga G., Taccini N. A wearable health care system based on knitted integrated sensors // IEEE Transactions on Information Technology in Biomedicine. 2005. Vol.9, Iss.3. P.337−344. https://doi.org/10.1109/TITB.2005.854512Paradiso R., Loriga G., Taccini N. A wearable health care system based on knitted integrated sensors. IEEE Transactions on Information Technology in Biomedicine 2005; 9(3):337−344. https://doi.org/10.1109/TITB.2005.854512Zakeri V., Akhbardeh A., Alamdari N., Fazel-Rezai R., Paukkunen M., Tavakolian K. Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases // IEEE Trans. Biomed. Eng. 2017. Vol.64, Iss.8. P.1786−1792. https://doi.org/10.1109/TBME.2016.2621037Zakeri V., Akhbardeh A., Alamdari N., Fazel-Rezai R., Paukkunen M., Tavakolian K. Analyzing Seismocardiogram Cycles to Identify the Respiratory Phases. IEEE Trans. Biomed. Eng. 2017; 64(8):1786−1792. https://doi.org/10.1109/TBME.2016.2621037Tavakolian K., Vaseghi A., Kaminska B. Improvement of ballistocardiogram processing by inclusion of respiration information // Physiol. Meas. 2008. Vol.29, Iss.7. P.771−781. https://doi.org/10.1088/0967-3334/29/7/006Tavakolian K., Vaseghi A., Kaminska B. Improvement of ballistocardiogram processing by inclusion of respiration information. Physiol. Meas. 2008; 29(7):771−781. https://doi.org/10.1088/0967-3334/29/7/006Balali P., Rabineau J., Hossein A., Tordeur C., Debeir O., van de Borne P. Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review // Sensors (Basel). 2022. Vol.22, Iss.23. Article number: 9565. https://doi.org/10.3390/s22239565Balali P., Rabineau J., Hossein A., Tordeur C., Debeir O., van de Borne P. Investigating Cardiorespiratory Interaction Using Ballistocardiography and Seismocardiography-A Narrative Review. Sensors (Basel) 2022; 22(23):9565. https://doi.org/10.3390/s22239565Weichao Zh., Hongbo N., Xingshe Zh., Yalong S., Tianben W. Identifying sleep apnea syndrome using heart rate and breathing effort variation analysis based on ballistocardiography // Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2015. P.4536−4539. https://doi.org/10.1109/EMBC.2015.7319403Weichao Zh., Hongbo N., Xingshe Zh., Yalong S., Tianben W. Identifying sleep apnea syndrome using heart rate and breathing effort variation analysis based on ballistocardiography. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2015:4536−4539. https://doi.org/10.1109/EMBC.2015.7319403Huysmans D., Borzée P., Testelmans D., Buyse B., Willemen T., Huffel S.V., Varon C. Evaluation of a Commercial Ballistocardiography Sensor for Sleep Apnea Screening and Sleep Monitoring // Sensors (Basel). 2019. Vol.19, Iss.9. Article number: 2133. https://doi.org/10.3390/s19092133Huysmans D., Borzée P., Testelmans D., Buyse B., Willemen T., Huffel S.V., Varon C. Evaluation of a Commercial Ballistocardiography Sensor for Sleep Apnea Screening and Sleep Monitoring. Sensors (Basel) 2019; 19(9):2133. https://doi.org/10.3390/s19092133Di Rienzo M., Vaini E., Lombardi P. An algorithm for the beat-to-beat assessment of cardiac mechanics during sleep on Earth and in microgravity from the seismocardiogram // Sci. Rep. 2017. Vol.7, Iss.1. Article number: 15634. https://doi.org/10.1038/s41598-017-15829-0Di Rienzo M., Vaini E., Lombardi P. An algorithm for the beat-to-beat assessment of cardiac mechanics during sleep on Earth and in microgravity from the seismocardiogram. Sci. Rep. 2017; 7(1):15634. https://doi.org/10.1038/s41598-017-15829-0Castiglioni P., Meriggi P., Rizzo F., Vaini E., Faini A., Parati G., Di Rienzo M. Seismocardiography while sleeping at high altitude // Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2012. P.3793−3796. https://doi.org/10.1109/EMBC.2012.6346793Castiglioni P., Meriggi P., Rizzo F., Vaini E., Faini A., Parati G., Di Rienzo M. Seismocardiography while sleeping at high altitude. Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. 2012:3793−3796. https://doi.org/10.1109/EMBC.2012.6346793Tafur E., Cohen L.S., Levine H.D. (1964). The Normal Apex Cardiogram: Its Temporal Relationship to Electrical, Acoustic, and Mechanical Cardiac Events // Circulation. 1964. Vol.30, Iss.3. P.381–391. https://doi.org/10.1161/01.cir.30.3.381Tafur E., Cohen L.S., Levine H.D. (1964). The Normal Apex Cardiogram: Its Temporal Relationship to Electrical, Acoustic, and Mechanical Cardiac Events. Circulation 1964; 30(3):381–391. https://doi.org/10.1161/01.cir.30.3.381Фатенков В.Н., Фатенков О.В. Новое в биомеханике сердца, артерий и малого круга кровообращения: монография. Самара: Ас Гард, 2012. 330 с. ISBN: 978-5-4259-0173-6.Fatenkov V.N., Fatenkov О.V. [New in the biomechanics of the heart, arteries and lesser circulation circuit]. Samara: As Gard; 2012 (in Russian). ISBN: 978-5-4259-0173-6.Козинский Н.А., Люсов В.А., Странин В.Г. Апекскардиография в диагностике безболевой ишемии миокарда // Российский кардиологический журнал. 2004. №4. C.69‒73. EDN: INTUAT.Kozinskiy N.A., Lyusov V.A., Stranin V.G. [Apex cardiography in silent myocardial ischemia diagnostics]. Russian Journal of Cardiology 2004; (4):69−73 (in Russian).Юзбашев З.Ю., Майскова Е.А. Методы исследования сердца, основанные на регистрации низкочастотных колебаний прекардиальной зоны, их диагностические возможности и перспективы // Научное обозрение. Медицинские науки. 2017. №5. С.74−94. EDN: ZCRUOB.Yuzbashev Z.Yu., Mayskova E.A. [Examination methods of the heart based on the registration of low-frequency vibrations of the precordial zone and their diagnostic potentials and perspectives]. Scientific Review. Medical Sciences. 2017; (5):74−94 (in Russian).Большая Медицинская Энциклопедия (БМЭ), под редакцией Петровского Б.В., 3-е издание. Т.15. URL: https://бмэ.орг/index.php/МЕХАНОКАРДИОГРАФИЯ[The Great Medical Encyclopedia]. 3rd edition (in Russian). Vol.15. Available at: https://бмэ.орг/index.php/МЕХАНОКАРДИОГРАФИЯОранский И.Е. Акселерационная кинетокардиография. М.: Медицина, 1973. 100 с. ISBN: 978-00-1370232- 0.Oranskii I.E. [Acceleration Kinetocardiography]. Мoscow: Meditsina; 1973 (in Russian). ISBN: 978-00-1370232- 0.Уткина А.В., Изотова А.Г., Литвинова Н.А. Алгоритм оценки частоты дыхания по сигналу ЭКГ // Modern Science. 2020. №5-1. С.415−421. EDN: ODMURZ.Utkina A.V., Izotova A.G., Litvinova N.A. [Algorithm of respiration rate estimation according to ECG signal] Modern Science 2020; (5-1):415−421 (in Russian).Ремизова Н. М. Динамика временных и спектральных характеристик интервала QT ЭКГ при изменении режима и частоты дыхания // Вестник Чувашского государственного педагогического университета им. И.Я.Яковлева. 2016. №3(91). C.18−24. EDN: WKUUTX.Remizova N.M. [Dynamics of temporal and spectral characteristics of QT interval of ECG when changing the mode and rate of breathing]. I. Yakovlev Chuvash State Pedagogical University Bulletin 2016; (3):18−24 (in Russian).Massaroni C., Nicolò A., Lo Presti D., Sacchetti M., Silvestri S., Schena E. Contact-Based Methods for Measuring Respiratory Rate // Sensors (Basel). 2019. Vol.19, Iss.4. Article number: 908. https://doi.org/10.3390/s19040908Massaroni C., Nicolò A., Lo Presti D., Sacchetti M., Silvestri S., Schena E. Contact-Based Methods for Measuring Respiratory Rate. Sensors (Basel) 2019; 19(4):908. https://doi.org/10.3390/s19040908Способ оценки вазомоторной функции эндотелия с применением объемной сфигмографии: пат. 2428924 RU / авторы и заявители А.Н.Рогоза, А.Р.Заирова, Е.В.Ощепкова; патентообладатель ФГУ "РКНПК" Минздравсоцразвития РФ (RU). 2011.Rogoza A.N., Zairova A.R., Oshhepkova E.V. Patent 2428924 RU. [A method for assessing the vasomotor function of the endothelium using volumetric sphygmography]; published 2011 (in Russian).Заирова А.Р., Рогоза А.Н. Объемная сфигмография сегодня // Медицинский алфавит. 2018. Т.4, №36. C.8−18. EDN: YXZOJN.Zairova A. R., Rogoza A. N. [Volume sphygmography today]. Medical alphabet. 2018; 4(36):8−18 (in Russian).Агаджанян Н.А., Власова И.Г., Ермакова Н.В., Торшин В.И. Основы физиологии человека / под ред. Н.В.Торшина. М.: РУДН, 2016. Т.1. 443 с. ISBN: 978-5-209-05301-9.Agadzhanian N.А., Vlasova I.G., Еmakova N.V., Тоrshin V.I. [Fundamentals of human physiology. Vol.1]. Мoscow: RUDN; 2016 (in Russian). ISBN: 978-5-209-05301-9.Yuda E., Shibata M., Ogata Y., Ueda N., Yambe T., Yoshizawa M., Hayano J. Pulse rate variability: a new biomarker, not a surrogate for heart rate variability // J. Physiol. Anthropol. 2020. Vol.39, Iss.1. Article number: 21. https://doi.org/10.1186/s40101-020-00233-xYuda E., Shibata M., Ogata Y., Ueda N., Yambe T., Yoshizawa M., Hayano J. Pulse rate variability: a new biomarker, not a surrogate for heart rate variability. J. Physiol. Anthropol. 2020; 39(1):21. https://doi.org/10.1186/s40101-020-00233-xФедотов А.А., Акулова А.С. Исследование сфигмографического измерительного преобразователя пульсовой волны // Вестник Самарского университета. Аэрокосмическая техника, технологии и машиностроение. 2015. Т.14, №4. C.192−199. EDN: VHRWHZ. https://doi.org/10.18287/2412-7329-2015-14-4-192-199Fedotov А.А., Akulova A.S. [Research of a pulse wave sphygmographic transducer]. Vestnik of Samara University. Aerospace and Mechanical Engineering 2015; 14(4):192−199 (in Russian). https://doi.org/10.18287/2412-7329-2015-14-4-192-199Лоллини С.В. Физиология сердечно-сосудистой системы. Учебно-методическое пособие. Витебск: УО «ВГУ им. П.М.Машерова», 2007. 62 с. ISBN: 978-985-425-843-0. URL: https://rep.vsu.by/handle/123456789/1925Lollini S.V. [Physiology of cardiovascular system]. Vitebsk; 2007 (in Russian). ISBN: 978-985-425-843-0. Available at: https://rep.vsu.by/handle/123456789/1925Брин В.Б., Захаров Ю.М., Мазинг Ю.А., Недоспасов В.О., Пятин В.Ф., Ткаченко Б.И. Нормальная физиология: учебник / под ред. Б.И.Ткаченко. 3-е изд., испр. и доп. М.: ГЭОТАР-Медиа, 2016. 688 c. ISBN: 978-5-9704- 3664-6. URL: http://www.studentlibrary.ru/book/ISBN9785970436646.htmlBrin V.B., Zakharov Yu.M., Mazing Yu.A., Nedospasov V.O., Pyatin V.F., Tkachenko B.I. [Normal physiology: textbook]. 3rd edition. Мoscow: GEOTAR-Media; 2016 (in Russian). ISBN: 978-5-9704-3664-6. Available at: http://www.studentlibrary.ru/book/ISBN9785970436646.htmlВолков Ю.Н., Большов В.М., Сингаевский С.Б., Земцовский Э.В., Гусейнов Б.А. Комплексная оценка функционального состояния систем кровообращения и дыхания методом интегральной реографии тела. Методические рекомендации. М.: МЗ СССР, 1989. 21 с.Volkov Yu.N., Bolshov V.М., Singaevskiy S.B., Zemtsovskiy E.V., Guseinov B.А. [Comprehensive assessment of the functional state of the circulatory and respiratory systems by integral rheography of the body: methodological recommendations]. Мoscow; 1989 (in Russian).Убайдуллаев А.М., Каримов Д.С., Якимова М.А. Основные методы исследования в диагностике заболеваний органов дыхания. Ташкент: Медицина, 1985. 75 с.Ubaydullaev A.M., Karimov D.S., Yakimova M.A. [The main research methods in the diagnosis of respiratory diseases]. Tashkent: Meditsina; 1985 (in Russian).Ревенко С.В. Гармонические перспективы реографии // Нервно-мышечные болезни. 2012. №4. С.8−18. EDN: PTWPUX.Revenko S.V. [Rheography: Harmonic perspectives]. Neuromuscular Diseases 2012; (4):8−18 (in Russian).

The authors declare that there are no conflicts of interest present.