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Modeling and analysis of systolic and diastolic blood pressure using ECG and PPG signals

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Warianty tytułu
PL
Modelowanie i analiza skurczowego i rozkurczowego ciśnienia krwi z wykorzystaniem sygnałów EKG i PPG
Języki publikacji
EN
Abstrakty
EN
Taking into account the peculiarities of using the MAX86150 evaluation system for measuring ECG and PPG signals, mathematical models were developed for indirect determination of systolic and diastolic pressure using fingers on the hand, which were tested in the MATLAB environment. Received ECG and PPG signals. Based on the proposed mathematical models, ECG and PPG signals were processed in the MATLAB package and the results of indirect measurement of blood pressure were presented.
PL
Biorąc pod uwagę specyfikę wykorzystania systemu oceny MAX86150 do pomiaru sygnałów EKG i PPG, opracowano modele matematyczne do pośredniego określania ciśnienia skurczowego i rozkurczowego używając palców dłoni, które zostały przetestowane w środowisku MATLAB. Otrzymano sygnały EKG i PPG. W oparciu o zaproponowane modele matematyczne, sygnały EKG i PPG zostały przetworzone w pakiecie MATLAB oraz przedstawiono wyniki pośredniego pomiaru ciśnienia krwi.
Rocznik
Strony
5--10
Opis fizyczny
Bibliogr. 35 poz., fot., tab., wykr.
Bibliografia
  • [1] Asgharnezhad H., Shamsi A., Bakhshayeshi I., Alizadehsani R., Chamaani S., Alinejad-Rokny H.: Improving PPG Signal Classification with Machine Learning: The Power of a Second Opinion. In IEEE 24th International Conference on Digital Signal Processing (DSP), 2023, 1–5.
  • [2] Chao P. C. P., Wu C. C., Nguyen D. H., Nguyen B. S., Huang P. C., Le V. H.: The machine learnings leading the cuffless PPG blood pressure sensors into the next stage. IEEE Sensors Journal 21(11), 2021, 12498–12510.
  • [3] Chiu Y. C., Arand P. W., Shroff S. G., Feldman T., Carroll J. D.: Determination of pulse wave velocities with computerized algorithms. American heart journal 121(5), 1991, 1460–1470.
  • [4] Dutt D., Shruthi S.: Digital processing of ECG and PPG signals for study of arterial parameters for cardiovascular risk assessment. In IEEE International conference on communications and signal processing (ICCSP), 2015, 1506–1510.
  • [5] Fortino G., Giampà V.: PPG-based methods for non invasive and continuous blood pressure measurement: an overview and development issues in body sensor networks. IEEE International Workshop on Medical Measurements and Applications, Ottawa, ON, Canada, 2010, 10–13.
  • [6] Gómez-Quintana S., Schwarz C. E., Shelevytsky I., Shelevytska V., Semenova O., Factor A., Popovici E., Temko A.: A framework for AI-assisted detection of patent ductus arteriosus from neonatal phonocardiogram. In Healthcare 9(2), 2021, 169.
  • [7] Haque C. A., Kwon T.-H., Kim K.-D.: Cuffless Blood Pressure Estimation Based on Monte Carlo Simulation Using Photoplethysmography Signals. Sensors 22, 2022, 1175.
  • [8] Kachuee M., Kiani M. M., Mohammadzade H., Shabany M.: Cuffless blood pressure estimation algorithms for continuous health-care monitoring. IEEE Transactions on Biomedical Engineering 64(4), 2016, 859–869.
  • [9] Kao Y. H., Chao P. C. P., Wey C. L.: Design and validation of a new PPG module to acquire high-quality physiological signals for high-accuracy biomedical sensing. IEEE J. Sel. Top. Quantum Electron 25, 2019, 18159167.
  • [10] Liang Y., Chen Z., Ward R., Elgendi M.: Hypertension assessment via ECG and PPG signals: An evaluation using MIMIC database. Diagnostics 8(3), 2018, 65.
  • [11] Man P. K., Cheung K. L., Sangsiri N., Shek W. J., Wong K. L., Chin J. W., Chan T. T., So R. H. Y.: Blood Pressure Measurement: From Cuff-Based to Contactless Monitoring. In Healthcare 10(10), 2022, 2113.
  • [12] Morresi N., Casaccia S., Sorcinelli M., Arnesano M., Revel G.: Analysing performances of Heart Rate Variability measurement through a smartwatch. In 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2020, 1–6.
  • [13] Mukkamala R., Hahn J. O., Inan O. T., Mestha L. K., Kim C. S., Töreyin H., Kyal S.: Toward Ubiquitous Blood Pressure Monitoring via Pulse Transit Time: Theory and Practice. In IEEE Transactions on Biomedical Engineering 62(8), 2015, 1879–1901.
  • [14] Payne R. A., Symeonides C. N., Webb D. J., Maxwell S. R.: Pulse transit time measured from the ECG: An unreliable marker of beat-to-beat blood pressure. J. Appl. Physiol. 100, 2006, 136–141.
  • [15] Pour Ebrahim M., Heydari F., Wu T., Walker K., Joe K., Redoute J. M., Yuce M. R.: Blood pressure estimation using on-body continuous wave radar and photoplethysmogram in various posture and exercise conditions. Scientific Reports 9(1), 2019, 1–13.
  • [16] Rundo F., Petralia S., Fallica G., Conoci S.: A nonlinear pattern recognition pipeline for PPG/ECG medical assessments. In Convegno Nazionale Sensori, 2018, 473–480.
  • [17] Samimi H., Dajani H. R.: Cuffless Blood Pressure Estimation Using Calibrated Cardiovascular Dynamics in the Photoplethysmogram. Bioengineering 9(9), 2022, 446.
  • [18] Semenov A., Osadchuk O., Semenova O., Bisikalo O., Vasilevskyi O., Voznyak O.: Signal Statistic and Informational Parameters of Deterministic Chaos Transistor Oscillators for Infocommunication Systems. 2018 International Scientific-Practical Conference Problems of Infocommunications Science and Technology, 2019, 8632046, 730–734.
  • [19] Shabaan M., Arshid K., Yaqub M., Jinchao F., Zia M., Bojja G., Iftikhar M., Ghani U., Ambati L., Munir R.: Survey: smartphone-based assessment of cardiovascular diseases using ECG and PPG analysis. BMC medical informatics and decision making, 2020, 1–6.
  • [20] Sharma M., Barbosa K., Ho V., Griggs D., Ghirmai T., Krishnan S. K., Hsiai T. K., Chiao J. C., Cao H.: Cuff-less and continuous blood pressure monitoring: a methodological review. Technologies 5(2), 2017, 21.
  • [21] Trishch R., Nechuiviter O., Dyadyura K., Vasilevskyi O., Tsykhanovska I., Yakovlev M.: Qualimetric method of assessing risks of low quality products. MM Science Journal 2021(4), 2021, 4769–4774.
  • [22] Tseng T. J., Tseng C. H.: Cuffless blood pressure measurement using a microwave near-field self-injection-locked wrist pulse sensor. IEEE Trans. Microw. Theory Tech 68, 2020, 4865–4874.
  • [23] Vasilevskyi O. M., Yakovlev M. Y., Kulakov P. I.: Spectral method to evaluate the uncertainty of dynamic measurements. Technical Electrodynamics 4, 2017, 72–78.
  • [24] Vasilevskyi O. M.: A frequency method for dynamic uncertainty evaluation of measurement during modes of dynamic operation. International Journal of Metrology and Quality Engineering 6(2), 2015, 202.
  • [25] Vasilevskyi O. M.: Assessing the level of confidence for expressing extended uncertainty: a model based on control errors in the measurement of ion activity. Acta IMEKO 10(2), 2021, 199–203.
  • [26] Vasilevskyi O. M.: Calibration method to assess the accuracy of measurement devices using the theory of uncertainty. International Journal of Metrology and Quality Engineering 5(4), 2014, 403.
  • [27] Vasilevskyi O. M.: Metrological characteristics of the torque measurement of electric motors. International Journal of Metrology and Quality Engineering 8, 2017, 7.
  • [28] Vasilevskyi O., Koval M., Kravets S.: Indicators of reproducibility and suitability for assessing the quality of production services. Acta IMEKO 10(4), 2021, 54–61.
  • [29] Vasilevskyi O., Kulakov P., Kompanets D., Lysenko O. M., Prysyazhnyuk V., Wójcik W., Baitussupov D.: A new approach to assessing the dynamic uncertainty of measuring devices. Proc. SPIE 10808, 2018, 728–735.
  • [30] Vasilevskyi O., Voznyak O., Didych V., Sevastianov V., Ruchka O., Rykun V.: Methods for Constructing High-precision Potentiometric Measuring Instruments of Ion Activity. In 2022 IEEE 41st International Conference on Electronics and Nanotechnology (ELNANO), 2022, 247–252.
  • [31] Wang H. S. J., Yeh M. H., Chao P. C. P., Tu T. Y., Kao Y. H., Pandey R.: A fast chip implementing a real-time noise resistant algorithm for estimating blood pressure using a non-invasive, cuffless PPG sensor. Microsyst. Technol 26, 2020, 3501–3516.
  • [32] Zhang Q., Zeng X., Hu W., Zhou D.: A Machine Learning-Empowered System for Long-Term Motion-Tolerant Wearable Monitoring of Blood Pressure and Heart Rate With Ear-ECG/PPG. In IEEE Access 5, 2017, 10547–10561.
  • [33] American Heart Association. [https://www.heart.org/en/] (access 08/07/2023).
  • [34] AnalogDevices Homepage [https://www.analog.com/media/en/technical-documentation/data-sheets/MAX86150EVSYS.pdf] (access 2023/08/07).
  • [35] THINKLABS Homepage [https://www.thinklabs.com/] (access 2023/08/07).
Uwagi
Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-59226014-9312-4397-bcd1-fcf3dc19ec16
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