Journal of the Korea Convergence Society (한국융합학회논문지)

Korea Convergence Society (한국융합학회)
권호 표지
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 (Division of Biomedical Engineering, Keimyung University)
  • 김선칠 (계명대학교 의용공학과)
  • Received : 2018.12.19
  • Accepted : 2019.02.20
  • Published : 2019.02.28
Download PDF
⟨ Previous Next ⟩

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.

혈압측정은 오랜 시간동안 외부압력을 이용하여 혈관 압력 대응값으로 계산해왔다. 최근 측정 장비의 소형화와 의료네트워크 기술의 발전으로 개인 건강관리시스템의 활성화로 인해 간단한 센서로 혈압을 연속적이며 실시간 측정이 가능한 환경을 요구하고 있다. 본 연구에서는 광전용적맥파를 적용하고 맥파전달시간을 이용하여 혈압을 추정하고자 한다. 기존 방식은 신체 변수값 등으로 개인 오차를 줄여 측정하는 알고리즘을 사용하고 있으나, 광전용적맥파의 분석과 맥파전달시간의 적용방법에 따라 정확도가 떨어진다. 본 연구에서는 기존 수축기 혈압을 이용하여 혈압을 유추하는 융합적인 방법을 선택하여 적용하였다. 그리고 광전용적맥파 자체로만 혈압 추정이 가능하게 구성하여 초소형 혈압측정시스템을 만드는데 필요한 융합알고리즘을 제공하고자 하였다. 그 결과 수축기혈압과 광전용적맥파의 최대, 최소 주기간격을 이용하여 혈압추정 융합 알고리즘의 가능성을 상관관계로 분석하였다.

Keywords

OHHGBW_2019_v10n2_67_f0002.png 이미지

Fig. 1. Characteristics of PPG waveform

OHHGBW_2019_v10n2_67_f0003.png 이미지

Fig. 2. PPG Measurement circuit

OHHGBW_2019_v10n2_67_f0004.png 이미지

Fig. 3. PPG Measurement method

OHHGBW_2019_v10n2_67_f0005.png 이미지

Fig. 4. PPG Measurement display

OHHGBW_2019_v10n2_67_f0006.png 이미지

Fig. 5. Associations of PTT with SBP, S1-P, P-S2

Table 1. Results of PTT measurement

OHHGBW_2019_v10n2_67_t0001.png 이미지

Table 2. Results of PTT Analysis

OHHGBW_2019_v10n2_67_t0002.png 이미지

Table 3. Associations of PTT with SBP, S1-P, P-S2 by multiple linear regression analyses

OHHGBW_2019_v10n2_67_t0003.png 이미지

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
  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
  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
  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
  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
  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
  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
  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