Abstract
Metabolic syndrome is not only a major public health problem in the United States and other developed countries but also is associated with increased risk for diabetes and coronary heart disease (CHD). This study presents a easy method for metabolic syndrome detection by measuring the digital volume pulse through the finger photoplethysmography (PPG). The trajectory analysis (TA) has been adopted to produce Poincare plots with the PPG signals. The Poincare plots are two-dimensional graphical representations (scatter plots) of PPG signals. The standard deviation along the longitudinal axis (SD2) in the plots are larger for the subjects with metabolic syndrome than who are without metabolic syndrome.
SD2 in the Poincare map of finger plethysmographic singnals is a good indicator to detect metabolic syndrome. In this study, we hope to develop a simple detecting indicator of metabolic syndrome, and find a simple way to judge metabolic syndrome through the finger pulse infrared sensor for the non-linear analysis of Poincaré plot. The indicator can remind the user to pay attention to their health, control of eating and living habits to reduce future serious chronic diseases at any time.
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J.-P. Despres, P. Poirier, J. Bergeron, A. Tremblay, I. Lemieux, and N. Almeras “From individual risk factors and the metabolic syndrome to global cardiometabolic risk”, Eur. Heart J. Suppl., March 1, 2008; 10(suppl_B): B24–B33.
Lakka HM, Laaksonen DE, Lakka TA, et al. The metabolic syndrome and total and cardiovascular disease mortality in middle-aged men. JAMA. 2002; 288: 2709–2716.
Ju-Yi Chen; Wei-Chuan Tsai; Ming-Sheng Wu; Chih-Hsin Hsu; Chih-Chan Lin; Hsien-Tsai Wu; Li-Jen Lin; Jyh-Hong Chen. Novel Compliance Index Derived from Digital Volume Pulse Associated with Risk Factors and Exercise Capacity in Patients Undergoing Treadmill Exercise Tests. Journal of Hypertension 2007, 25; 1894–1899
Wei-Chuan Tsai, Ju-Yi Chen, Ming-Chen Wang, Hsien-Tsai Wu, Chih-Kai Chi, Yung-Kung Chen, Jyh-Hong Chen, and Li-Jen Lin, “Association of Risk Factors With Increased Pulse Wave Velocity Detected by a Novel Method Using Dual-Channel Photoplethysmography”, AJH, 8, pp.1118–1122, 2005.
Philip J. Chowienczyk, Ronan P. Kelly, Helen MacCallum, Sandrine C. Millasseau, Tomas L. G. Andersson, Raymond G. Gosling, James M. Ritter and Erik E. Änggård. Photoplethysmographic assessment of pulse wave reflection: Blunted response to endothelium-dependent beta 2-adrenergic vasodilation in type II diabetes mellitus. J. Am. Coll. Cardiol. 1999;34;2007–2014
Hsien-Tsai Wu, Cheng-Tso Yao, Tsang-Chih Wu and An-Bang Liu, “Development of Easy Operating Arterial Stiffness Assessment Instrument for Homecare”, The 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, France, 23–26, Aug. 2007, pp5868–5871
Heikki V. Huikuri, Timo H. Mäkikallio, Chung-Kang Peng, Ary L. Goldberger, Ulrik Hintze and Mogens Møller. Fractal Correlation Properties of R-R Interval Dynamics and Mortality in Patients With Depressed Left Ventricular Function After an Acute Myocardial Infarction, Circulation 2000;101;47–53.
Phyllis K, Stein and Anand, Reddy (2005) Non-Linear Heart Rate Variability and Risk Stratification in Cardiovascular Disease. Indian Pacing Electrophysiol J. 2005 Jul-Sep; 5(3): 210–220.
Andreas Voss, Rico Schroeder, Sandra Truebner, Matthias Goerning, Hans Reiner Figulla and Alexander Schirdewan. Comparison of nonlinear methods symbolic dynamics, detrended fluctuation, and Poincare plot analysis in risk stratification in patients with dilated cardiomyopathy, Chaos 17, 015120 (2007); DOI:10.1063/1.2404633.
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© 2009 International Federation of Medical and Biological Engineering
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Wu, HT. et al. (2009). Implementation of Trajectory Analysis System for Metabolic Syndrome Detection. In: Lim, C.T., Goh, J.C.H. (eds) 13th International Conference on Biomedical Engineering. IFMBE Proceedings, vol 23. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-92841-6_153
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DOI: https://doi.org/10.1007/978-3-540-92841-6_153
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