DOI QR코드

DOI QR Code

Analysis of Blood pressure influence factor Correction for Photoplethysmography Fusion Algorithm Calibration

광전용적맥파 융합 알고리즘 보정을 위한 혈압 영향인자 상관관계 분석

Kim, Seon-Chil
김선칠

  • Received : 2018.12.19
  • Accepted : 2019.02.20
  • Published : 2019.02.28

Abstract

The blood pressure measurement is calculated as a value corresponding to the pressure of the blood vessel using the pressure from the outside for a long time. Due to the recent miniaturization of measurement equipment and the ICT combination of personal healthcare systems, a system that enables continuous and real-time measurement of blood pressure with a sensor is required. In this study, blood pressure was measured using pulse transit time using Photoplethysmography. In this study, blood pressure was estimated by using systolic blood pressure. And it is possible to make measurement only with PPG itself, which can contribute to making a micro blood pressure measuring device. As a result, systolic blood pressure and PPG's S1-P and P-S2 were used to analyze the possibility of blood pressure estimation.

Keywords

Convergence Algorithm;Electrocardiogram;Photoplethysmography;Bood Pressure;Pulse Transit Time

References

  1. A. Onta, K. Ceyhan K, O. Basar, B. Erer, S. Tobrak, V. Sansoy. (2002). Metabolic syndrome: major impact on coronary risk in a population with low cholesterol levels: a prospective and cross-sectional evaluation. Athrosclerosis, 165(2), 285-292. DOI : 10.1016/S0021-9150(02)00236-8 https://doi.org/10.1016/S0021-9150(02)00236-8
  2. S. Lim. H. K. Lee. K. S. Park. & S. I. Cho. (2005). Changes in the characteristics of metabolic syndrome in Korea over the period 1998-2001 as determined by korea national health and nutrition Examination Surveys. Diabetes Care, 28(7), 1810-1812. DOI : 10.2337/diacare.28.7.1810 https://doi.org/10.2337/diacare.28.7.1810
  3. S. G. Beak & D. J. Kim (2018). Relationship between Muscular Extension Exercise and Metabolic Syndrome Indices in Hypertensive Patients Journal of the Korea Convergence Society, 9(9), 363-369. DOI : 10.15207/JKCS.2018.9.9.363 https://doi.org/10.15207/JKCS.2018.9.9.363
  4. W. H. Choi, Y. M. Seo, M. Y. Jeon & S. Y. Choi (2018). Convergence Study on the Comparison of Risk Factors for Dyslipidemia by Age and Gender: Based on the Korea National Health and Nutrition Examination Survey(2013-2015year), Journal of the Korea Convergence Society, 9(10), 571-587. DOI :10.15207/JKCS.2018.9.10.571 https://doi.org/10.15207/JKCS.2018.9.10.571
  5. W. Chen. T. Kobayashi. S. Ichikawa. Y. Takeuchi. & T. Togawa. (2000). Continuous estimation of systolic blood pressure using the pulse arrival time and intermittent calibration. Medical & Biological Engineering & Computing, 38(5), 569-574. DOI : 10.1007/BF02345755 https://doi.org/10.1007/BF02345755
  6. E. J. Jung, J. C. Kim, H. I. Jung, H. Yoo & K. Y. Chung. (2017). Mining based Mental Health and Blood Pressure Management Service for Smart Health, Journal of the Korea Convergence Society, 8(3), 13-18. DOI :10.15207/JKCS.2017.8.1.013 https://doi.org/10.15207/JKCS.2017.8.1.013
  7. J. M. Bruner. (1984). Automated indirect blood pressure measurement a point of view. Medical Instrumentation, 18(2), 143-145.
  8. K. W. Chan. & Y. T. Zhang. (2001). Noninvasive and cuffless measurements of blood pressure for telemedicine. 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 3592-3593. DOI : 10.1109/IEMBS.2001.1019611
  9. J. V. Egmond. M. Senbos & J. F. Crul. (1985). Invasive v. non-invasive measurement of arterial pressure : comparison of two automatic methods and simultaneously measured direct intra-arterial pressure, British Journal of Anaesthesia, 57(4), 434-444. DOI : 10.1093/bja/57.4.434 https://doi.org/10.1093/bja/57.4.434
  10. H. H. Asada. P. Shaltis. A. Reisner. S. Rhee & R. Hutchinson. (2003). Mobile monitoring with wearable photoplethysmographic biosensors. IEEE Engineering in Medicine and Biology Magazine, 22(3), 28-40. DOI : 10.1109/MEMB.2003.1213624
  11. K. Meigas, R. Attai & J. Lass. (2001). Continuous blood pressure monitoring using pulse wave delay, 2001 Conference Proceedings of the 23rd Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 25-28. DOI : 10.1109/IEMBS.2001.1019495
  12. J. H. Kim, M. C. Whang & K. C. Nam. (2008). Development of continuous blood pressure measurement system using ECG and PPG, Korean Journal of the Science of Emotion & Sensibility, 11(2), 235-244.
  13. E. O'Brien. J. Petrie. W. Littler. M. D. Swiet. P. L. Padfield. D, G. Altman, M. Bland, A. Coats & N. Atkins. (1993). The british hypertension society protocol for the evaluation of blood pressure measuring devices. Journal of Hypertension, 11(2), 543-562. https://doi.org/10.1097/00004872-199305000-00010
  14. M. Kachuee. M. M. Kiani. H. Mohammadzade. M. Shabany. (2015). Cuff-less high-accuracy calibration-free blood pressure estimation using pulse transit time, 2015 IEEE International Symposium on Circuits and Systems (ISCAS), 24-27. DOI : 10.1109/ISCAS.2015.7168806
  15. Y. G. Gil. (2013). Development of model for blood pressure estimation using Multiple Bio-Signal and a real-time monitoring system based on IPv6. Doctoral dissertation. Busan University, Busan.
  16. M. Nitzan. B. Khanokh & Y. Slovik. (2001). The difference in pulse transit time to the toe and finger measured by photoplethysmography. Physiological Measurement, 23(1), 85-93 DOI : 10.1088/0967-3334/23/1/308
  17. S. M. Lee, E. K. Park, I. Y. Kim. & S. I. Kim. (2005). An estimating method for systolic blood pressure by using pulse transit time and physical characteristic parameters, The Institute of electronics Engineers of korea, 42(3), 41-46.
  18. P. A. Shaltis. A. Reisner. & H. H. Asada. (2006). Wearable, cuff-less PPG-based blood pressure monitor with novel height sensor. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 908-911. DOI : 10.1109/IEMBS.2006.260027