한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제10권8호
  • /
  • Pages.53-58
  • /
  • 2019
  • /
  • 2233-4890(pISSN)
  • /
  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
권호 표지
DOI QR코드

DOI QR Code

두 개의 광전용적맥파 기반의 수축기 혈압과 이완기 혈압 추정 융합 알고리즘 모델 분석

Analysis of the Convergence Algorithm Model for Estimating Systolic and Diastolic Blood Pressure Based on Two Photoplethysmography

  • 김선칠 (계명대학교 의용공학과) ;
  • 조성현 (남부대학교 물리치료학과)
  • Kim, Seon-Chil (Department of Biomedical Engineering, Keimyung University) ;
  • Cho, Sung-Hyoun (Department of Physical Therapy, Nambu University)
  • 투고 : 2019.06.05
  • 심사 : 2019.08.20
  • 발행 : 2019.08.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 만성질환자 건강관리의 목적으로 혈압측정에 대한 접근성을 높이는 제품 연구가 지속적으로 이루어지고 있다. 기존 연구에서는 심전도(ECG)와 광전용전맥파(PPG)를 분석하여 수축기혈압과 이완기 혈압을 산출하는 방식을 사용하고 있다. 주 과제는 정확도와 재현성을 위한 분석 알고리즘 개발이다. 본 연구에서는 초소형 혈압측정장치를 개발하는 단계에서 장치의 크기를 줄이고 측정방법도 간단히 하는 동시에 알고리즘도 두 개의 PPG만을 이용하여 최고혈압(SBP)을 추출하고 이에 따른 최저혈압(DBP)을 구하고자 하였다. 이를 위해 두 개의 PPG에서 얻은 측정값과 SBP, DBP 관계를 통계적으로 추적하여 상호관계를 분석하였다. PPG의 차이 값인 DF_P는 SBP와 반비례 관계가 있으며, DBP와는 비례적 관계가 성립되어 알고리즘에 의해 혈압값을 유추할 수 있으며, SBP를 통해 DBP를 추적할 수 있다.

Recently, product research has been continuously conducted to enhance accessibility to blood pressure measurement for the purpose of healthcare for the chronic patient. In previous studies, electrocardiogram (ECG) and photoelectric pulse wave (PPG) are analyzed to calculate systolic and diastolic blood pressure. The problem is the development of analysis algorithms for accuracy and reproducibility. In this study, in the development stage of a micro blood pressure measuring device, the size of the device was reduced and the measurement method was simplified, while the algorithm was to extract systolic blood pressure (SBP) using only two PPGs and obtain diastolic blood pressure (DBP). The difference value of PPG, DF_P, is inversely related to SBP, and has a proportional relationship with DBP, which can be leaked by algorithm, and DBP can be tracked through SBP.

키워드

참고문헌

  1. W. Wu, J. T Lee & H. Chen. (2009). Estimation of Heart Rate Variability Changes during Different Visual Stimulations Using Non-invasive Continuous ECG Monitoring System, International Joint Conference on Bioinformatics, 344-347. DOI : 10.1109/IJCBS.2009.75
  2. 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
  3. M. Ezzati M, A. D. Lopez, A. Rodgers, C. J. L. Murray. (2004). Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors. Geneva: World Health Organization.
  4. E. J. Jung, J. C Kim, H. 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(1), 13-18. DOI : 10.15207/JKCS.2017.8.1.013
  5. L. A. Geddes et al. (1981). Pulse Transit Time as an Indicator of Arterial Blood Pressure, Psychophysiology, 18(1), 71-74. DOI : 10.1111/j.1469-8986.1981.tb01545.x
  6. K. W. Chan & Y. T. Zhang, (2001). Noninvasive and cuffless measurements of Blood pressure for telemedicine, Proceedings of the 23rd Annual EMBS Conference, 3592-3593. DOI : 10.1109/IEMBS.2001.1019611
  7. 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
  8. J. E. Naschitz, I. Rosner, N. Shaviv, I Khorshidi, & S. Sundick. (2003). Assessment of cardiovascular reactivity by fractal and recurrence quantification analysis of heart rate and pulse transit time, Journal of Hunam Hypertension, 17(2), 111-118. DOI: 10.1038/sj.jhh.1001517
  9. D. C. Zheng, & Y. T. Zhang. (2003). A ring-type Device for the Noninvasive Measurement of Arterial Blood Pressure, IEEE Engineering in Medicine and Biology Magazine, 17-21. DOI : 10.1109/IEMBS.2003.1280819
  10. R. Asmar et al. (1995). Assesment of arterial distensibility by automatic pulse-wave velocity measurement validation and clinical studies, Hypertension, 26(3), 485-490. DOI : 10.1161/01.HYP.26.3.485
  11. D. Lemogoum et al. (2004). Validity of pulse pressure and augmentation index as surrogate measures of arterial stiffness during beta-adrenergic stimulation. Journal of the Hypertens, 22(3), 511-517. DOI: 10.1097/00004872-200403000-00013
  12. J. Y. A. Foo, C. S. Lim, Wang & Ping. (2006). Evaluation of blood pressure changes using vascular transit time, Physiological Measurement, 27(8), 685-694. DOI: 10.1088/0967-3334/27/8/003
  13. C. Z. Myint, K. H. Lim & K. l. Wong. (2014), Alpha Agape Gopalai, Blood Pressure Measurement from Photo-plethysmography to Pulse Transit Time, IEEE Conference on Biomedical Engineering and Sciences. DOI : 10.1109/IECBES.2014.7047550
  14. Barschdorff & Zhang. (1994). Respiratory Rhythm Detection With Photoplethysmo-graphic Methods, Proceedings of 16th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 912-913. DOI : 10.1109/IEMBS.1994.415209
  15. 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
  16. J. E. Naschitz et al. (2004). Pulse Transit Time by R-Wave-Gated Infrared Photoplethysmography: Review of the Literature and Personal Experience, Journal of Clinical Monitoring and Computing, 18(5), 333-342. DOI : 10.1007/s10877-005-4300-z
  17. J. I. Ko et al. (2007). Compensation of Error in Noninvasive Blood Pressure Measurement System Using Optical Sensor, Journal of biomedical engineering research, 28(2), 178-186.