DOI QR코드

DOI QR Code

광용적맥파의 정량적 맥파형 분류에 관한 연구

A Study on the Quantitative Pulse Type Classification of the Photoplethysmography

  • 장대근 (한국과학기술원 전기및전자공학과) ;
  • 우말 파르크 (경희대학교 동서의료공학과) ;
  • 박승훈 (경희대학교 동서의료공학과) ;
  • 한민수 (한국과학기술원 전기및전자공학과)
  • Jang, Dae-Jeun (Department of Electrical Engineering, KAIST) ;
  • Farooq, Umar (Department of Biomedical Engineering, Kyung Hee University) ;
  • Park, Seung-Hun (Department of Biomedical Engineering, Kyung Hee University) ;
  • Hahn, Min-Soo (Department of Electrical Engineering, KAIST)
  • 투고 : 2010.06.21
  • 심사 : 2010.08.04
  • 발행 : 2010.09.30

초록

Over the past few years, a considerable number of methods have been proposed and applied for the classification of photoplethysmography (PPG). Most of the previous studies, however, focused on the qualitative description of the pulse type according to specific disease and thus provided ambiguous criteria to interpreters. In order to screen out this problem, we present a quantitative method for the pulse type classification including the second derivative of photoplethysmography (SDPTG). In the PPG signal, we have classified the signal as 4 types using the position and the presence of the dicrotic wave. In addition, we have categorized the SDPTG signal as 7 types using the position and the presence of "c" and "d" wave and the sign of "c" wave. In order to check the efficacy of the proposed pulse type classification rule, we collected pulse signals from 155 subjects with different ages and sex. From the correlation analysis, Class 1(p<0.01) and Class 2(p<0.01) in the PPG signal are significantly correlated with ages. In a similar manner Class A(p<0.01), Class C(p<0.05), Class D(p<0.01), and Class F(p<0.01) in the SDPTG signal are considerably correlated with the ages. From these observations, and some earlier ones [4], [5], we can conclude that since the newly proposed method has objectivity and clarity in pulse type classification, this method can be used as an alternative of previous classification rules including similar age-related characteristics.

키워드

참고문헌

  1. P.Y. Zhong and H.Y. Wang, "A Framework for Automatic Time-Domain Characteristic Parameters Extraction of Human Pulse Signals," EURASIP Journal on Advances in Signal Processing, vol. 2008, no. 55, pp. 1-9, 2008.
  2. P.H. Tsui et al., "Arterial pulse waveform analysis by the probability distribution of amplitude," Physiol. Meas., vol. 28, no. 8, pp. 803-812, 2007. https://doi.org/10.1088/0967-3334/28/8/004
  3. S.C. Millasseau, J.M. Ritter, K. Takazawa, and P.J. Chowienczyk, "Contour analysis of the photoplethysmographic pulse measured at the finger," Journal of Hypertension, vol. 24, no. 8, pp. 1449-1456, 2006. https://doi.org/10.1097/01.hjh.0000239277.05068.87
  4. T.R. Dawber, H.E. Jr. Thomas, and P.M. McNamara, "Characteristics of the dicrotic notch of the arterial pulse wave in coronary heart disease," Angiology, vol. 24, no. 4, pp. 244-255, 1973. https://doi.org/10.1177/000331977302400407
  5. Y. Sano et al., "Evaluation of peripheral circulation with accelerated plethysmography and its practical application- Quantification of inflection points of a waveform," Bull. Phys. Fitness Res. Inst., vol. 68, pp. 17-25, 1988.
  6. T.H. Kim et al., Biofunctional Medicine, Seoul, Korea: Koonja Press., 2008, pp. 49-80.
  7. Sean Walsh and Emma King, Pulse Diagnosis - A Clinical Guide, PA, USA: Elsevier, 2008, pp. 5-21.
  8. John Allen, "Photoplethysmograhpy and its application in clinical physiological measurement," Physiol. Meas., vol. 28, pp. R1-R39, 2007. https://doi.org/10.1088/0967-3334/28/3/R01
  9. K.I. Song and J.S. Choi, Clinical Data Analysis by SPSS 15 - A practical Guide for Clinicians, Seoul, Korea:Hannarae Publishing Co., 2009, pp. 71-77.
  10. B. Jano and C. Raikumar, "Ageing and vascular ageing," Postgrad Med J, vol. 82, pp. 357-362, 2006. https://doi.org/10.1136/pgmj.2005.036053
  11. R. Gonzalez et al., "A Computer Based Photoplethysmographic Vascular Analyzer through Derivatives," Computers in Cardiology, vol. 35, pp. 177-180, 2008.
  12. H. Wang and P. Zhang, "A Quantitative Method for Pulse Strength Classification Based on Decision Tree," Journal of Software, vol. 4, no. 4, pp. 323-330, 2009.
  13. S.H. Park and H.S. Hong, "A Study on the Auto-diagnosis Plethysmograph by Novel Algorithm for Radial Pulse Detection," J. of KOSOMBE, vol. 17, no. 2, pp. 241-245, 1996.
  14. C.M. McEniery et al., "Normal Vascular Aging: Differential Effects on Wave Reflection and Aortic Pulse Wave Velocity," Journal of the American College of Cardiology, vol. 46, no. 9, pp. 1753-1760, 2005. https://doi.org/10.1016/j.jacc.2005.07.037