한국융합학회논문지 (Journal of the Korea Convergence Society)

  • 제10권11호
  • /
  • Pages.209-215
  • /
  • 2019
  • /
  • 2233-4890(pISSN)
  • /
  • 2713-6353(eISSN)
한국융합학회 (Korea Convergence Society)
권호 표지
DOI QR코드

DOI QR Code

웨어러블 초소형 혈압계 개발을 위한 혈압 추정 융합 알고리즘 분석

Blood Pressure Estimation for Development of Wearable small Blood Pressure Monitor Fusion Algorithm Analysis

  • Kim, Seon-Chil (Department of Biomedical Engineering, Keimyung University) ;
  • Kwon, Chan-Hoe (Department of Medical School Chonbuk National University) ;
  • Park, You-rim (Department of Biomedical Engineering, Keimyung University)
  • 투고 : 2019.09.02
  • 심사 : 2019.11.20
  • 발행 : 2019.11.28
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

디지털헬스케어에서 가장 중요한 개인 건강관리는 주로 만성질환자에게 매우 중요한 문제이다. 따라서 실시간 건강관리를 위한 간단한 웨어러블 디바이스 개발이 중요하다. 기존 혈압 추정 웨어러블 디바이스는 PPG 특성을 통해 PTT를 분석하여 혈압 추정 알고리즘을 제안하고 있다. 그러나 PPG의 재현성과 여러 가지 PTT의 적용여부와 측정자의 신체적 차이에서 발생되는 변수 등 알고리즘의 영향인자가 사실 매우 복잡하다. 따라서 본 연구에서는 PTT와 SBP, DBP의 상관관계를 분석하고, 다바이스 소형화를 위해 PPG센서만을 사용하게 설계하였다. 제안된 혈압 추정 알고리즘은 PPG간의 차이와 심박수, 개인적인 변수 등을 고려하였다. 또한 기존 의료기관에서 사용하는 공기가압방식의 결과값과 개발된 알고리즘을 통해 추정된 혈압값과 비교를 통해 확인하였다.

The most important personal health care in digital health care is a very important issue mainly for chronic diseases. Therefore, it is important to develop a simple wearable device for real-time health management. Existing blood pressure estimation wearable devices use PPG characteristics to analyze PTT and propose blood pressure estimation algorithms. However, the influencing factors of the algorithm such as the reproducibility of PPG, whether to apply various PTTs, and variables generated from the physical differences of the measurers are actually very complex. Therefore, in this study, the correlation between PTT, SBP, and DBP was analyzed, and it was designed to use PPG sensors for device miniaturization. The blood pressure estimation algorithm took into account differences in PPG, heart rate, and personal variables.

키워드

참고문헌

  1. J. H. Park et al. (2019). Validation of Wearable Blood Pressure Monitoring and Effects of Clothing Microclimate on Blood Pressure in the Hypertensive Elderly. Journal of The Korean Society of Living Environmental System, 26(2), 239-253. DOI : 10.21086/ksles.2019.04.26.2.239
  2. NCD Risk Factor Collaboration. (2017). Worldwide trends in blood pressure from 1975 to 2015: a pooled analysis of 1479 population-based measurement studies with 19.1 million participants. Lancet, 389, 37-55. https://doi.org/10.1016/S0140-6736(16)31919-5
  3. Y. J. Kim. (2012). Exploratory Study on Acceptance Intention of Mobile Devices and Applications for Healthcare Services, Jouranl of the Korea Contents association, 12(9), 639-379. DOI : 10.5392/JKCA.2012.12.09.369
  4. U. Lee, H. Park & H. Shin. (2014). Implementation of a Bluetooth-LE Based Wireless ECG/EMG/ PPG Monitoring Circuit and System, Journal of The Institute of Electronics and Information Engineers, 51(6), 261-269. DOI : 10.5573/ieie.2014.51.6.261
  5. T. Tamura, Y. Maeda, M. Sekine & M. Yoshida. (2014). Wearable photoplethysmographic sensors Past and Present. Electronics, 3, 282-302. DOI : 10.3390/electronics3020282
  6. S. C. Kim. (2019). Analysis of Blood pressure influence factor Correction for Photoplethysmography Fusion Algorithm Calibration, Journal of the Korea Convergence Society, 10(2), 67-73. DOI : 10.15207/JKCS.2019.10.2.067
  7. M. H. Pollak & P. A. Obrist. (1983). Aortic-radial pulse transit time and ECG Q-Wave to radial pulse wave as indices of beat-to-beat blood pressure change, Psychophysiology, 20, 21-28. https://doi.org/10.1111/j.1469-8986.1983.tb00895.x
  8. S. C. Kim & S. H. Cho. (2019). Analysis of the Convergence Algorithm Model for Estimating Systolic and Diastolic Blood Pressure Based on Two Photoplethysmography, Journal of the Korea Convergence Society, 10(8), 1-6. https://doi.org/10.15207/JKCS.2019.10.8.001
  9. Y. Liang et al. (2019). How Effective Is Pulse Arrival Time for Evaluating Blood Pressure Challenges and Recommendations from a Study Using the MIMIC Database. Journal of Clinical Medicine, 8(3), 337-341. DOI : 10.3390/jcm8030337
  10. S. Y. Lee, J. H. Lee, M. K. Kim & B. G. Yoo. (2018). Fluid-Attenuated Inversion Recovery Hyperintense Vessels Due to Septic Shock, Journal of Neurosonol and Neuroimag, 10(2), 157-177. DOI : 10.31728/jnn.2018.00037
  11. B. Sangeeta & L. Shaw. (2011). A Real Time Analysis of PPG Signal for Measurement of SpO2 and Pulse Rate, International journal of computer applications, 36(11), 45-50.
  12. O. H. Gauer Kreislauf des blutes. (1960). Lehrbuch der Physiologie des Menschen, Landois L, Rosemann HU (Eds), Urban & Schwarzenberg, Munich, 95.
  13. O. S. Randall, N. Westerhof, G. C. van den Bos & B. Alexander. (1986). Reliability of stroke volume to pulse pressure ratio for estimating and detecting changes in arterial compliance, Journal of Hypertens, 4, 293-296
  14. T. Atlasz, L. Kellenyi & P. Kovacs. (2006). The application of surface plethysmography for heart rate variability analysis after GSM radiofrequency exposure, Journal of Biochem Biophys Methods. 69, 233-236. DOI : 10.1016/j.jbbm.2006.03.017
  15. P. Palatini & S. Julius. (1997). Review article: heart rate and the cardiovascular risk. Journal of Hypertens, 15, 3-17. https://doi.org/10.1097/00004872-199715010-00001
  16. D. G. Christofaro et al. (2017). Relationship between resting heart rate, blood pressure and pulse pressure in adolescents. Arquivos Brasileiros de Cardiologia, 108, 405-410. DOI : 10.5935/abc.20170050
  17. Y. Gil & J. Lee. (2015). Design and implementation of real-time blood pressure measuring system using smartphone. KIISE Transactions on Computing Practices, 21(3), 192-314. DOI : 10.5626/KTCP.2015.21.3.192
  18. J. H. Kim et al. (2008). Development of continuous blood pressure measurement system using ECG and PPG, Korea society for emotion and sensibility, 11(2), 235-244.