MODELING AND ANALYSIS OF SYSTOLIC AND DIASTOLIC BLOOD PRESSURE USING ECG AND PPG SIGNALS

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.

Słowa kluczowe:

ciśnienie skurczowe, ciśnienie rozkurczowe, sygnały EKG i PPG, pomiar, metoda określania ciśnienia krwi

Bibliografia

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. DOI: https://doi.org/10.1109/DSP58604.2023.10167869

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. DOI: https://doi.org/10.1109/JSEN.2021.3073850

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. DOI: https://doi.org/10.1016/0002-8703(91)90153-9

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. DOI: https://doi.org/10.1109/ICCSP.2015.7322766

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. DOI: https://doi.org/10.1109/MEMEA.2010.5480201

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. DOI: https://doi.org/10.3390/healthcare9020169

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. DOI: https://doi.org/10.3390/s22031175

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. DOI: https://doi.org/10.1109/TBME.2016.2580904

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. DOI: https://doi.org/10.1109/JSTQE.2018.2871604

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. DOI: https://doi.org/10.3390/diagnostics8030065

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. DOI: https://doi.org/10.3390/healthcare10102113

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. DOI: https://doi.org/10.1109/MeMeA49120.2020.9137211

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. DOI: https://doi.org/10.1109/TBME.2015.2441951

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. DOI: https://doi.org/10.1152/japplphysiol.00657.2005

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. DOI: https://doi.org/10.1038/s41598-019-52710-8

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. DOI: https://doi.org/10.1007/978-3-030-04324-7_57

Samimi H., Dajani H. R.: Cuffless Blood Pressure Estimation Using Calibrated Cardiovascular Dynamics in the Photoplethysmogram. Bioengineering 9(9), 2022, 446. DOI: https://doi.org/10.3390/bioengineering9090446

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. DOI: https://doi.org/10.1109/INFOCOMMST.2018.8632046

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. DOI: https://doi.org/10.1186/s12911-020-01199-7

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. DOI: https://doi.org/10.3390/technologies5020021

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. DOI: https://doi.org/10.17973/MMSJ.2021_10_2021030

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. DOI: https://doi.org/10.1109/TMTT.2020.3011446

Vasilevskyi O. M., Yakovlev M. Y., Kulakov P. I.: Spectral method to evaluate the uncertainty of dynamic measurements. Technical Electrodynamics 4, 2017, 72–78. DOI: https://doi.org/10.15407/techned2017.04.072

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. DOI: https://doi.org/10.1051/ijmqe/2015008

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. DOI: https://doi.org/10.21014/acta_imeko.v10i2.810

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. DOI: https://doi.org/10.1051/ijmqe/2014017

Vasilevskyi O. M.: Metrological characteristics of the torque measurement of electric motors. International Journal of Metrology and Quality Engineering 8, 2017, 7. DOI: https://doi.org/10.1051/ijmqe/2017005

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. DOI: https://doi.org/10.21014/acta_imeko.v10i4.814

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.

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. DOI: https://doi.org/10.1109/ELNANO54667.2022.9927128

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. DOI: https://doi.org/10.1007/s00542-020-04946-y

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. DOI: https://doi.org/10.1109/ACCESS.2017.2707472

American Heart Association. [https://www.heart.org/en/] (access 08/07/2023). DOI: https://doi.org/10.55041/IJSREM25025

AnalogDevices Homepage [https://www.analog.com/media/en/technical-documentation/data-sheets/MAX86150EVSYS.pdf] (access 2023/08/07).

THINKLABS Homepage [https://www.thinklabs.com/] (access 2023/08/07).

Vasilevskyi, O., Popovici, E., & Sarana, V. (2023). MODELOWANIE I ANALIZA SKURCZOWEGO I ROZKURCZOWEGO CIŚNIENIA KRWI Z WYKORZYSTANIEM SYGNAŁÓW EKG I PPG. Informatyka, Automatyka, Pomiary W Gospodarce I Ochronie Środowiska, 13(3), 5–10. https://doi.org/10.35784/iapgos.5326

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Numer Tom 13 Nr 3 (2023)

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