Arterial Stiffness Using Radial Arterial Waveforms Measured at the Wrist as an Indicator of Diabetic Control in the Elderly

Although current technique of photoplethysmography (PPG) is a popular noninvasive method of waveform contour analysis in assessing arterial stiffness, data obtained are frequently affected by various environmental and physiological factors. We proposed an easily operable air pressure sensing system (APSS) for radial arterial signal capturing. Totally, 108 subjects (young, the aged with or without diabetes) were recruited from July 2009 to May 2010. Arterial waveform signals from the wrist were obtained and analyzed using Hilbert-Huang transformation (HHT). Through ensemble empirical mode decomposition (EEMD), the signals were decomposed into eight intrinsic mode functions (IMF1-8) of which IMF5 was found to be the desired signal with a discernible diastolic peak. The results showed significant differences in reflection index (RI) and stiffness index (SI) from the young subjects and those from the aged participants with or without diabetes. Significant differences in RI and SI were also noted between subjects with well-controlled diabetes and those without. Good reproducibility and correlation were demonstrated. In conclusion, the present study proposed the application of radial arterial signal capturing subsystem and HHT in acquiring more reliable data on RI and SI compared with the conventional PPG method.

[1]  Stephen R. Alty,et al.  Predicting Arterial Stiffness From the Digital Volume Pulse Waveform , 2007, IEEE Transactions on Biomedical Engineering.

[2]  P. Zimmet,et al.  Definition, diagnosis and classification of diabetes mellitus and its complications. Part 1: diagnosis and classification of diabetes mellitus. Provisional report of a WHO Consultation , 1998, Diabetic medicine : a journal of the British Diabetic Association.

[3]  Men-Tzung Lo,et al.  Nonlinear pressure-flow relationship is able to detect asymmetry of brain blood circulation associated with midline shift. , 2009, Journal of neurotrauma.

[4]  Hsien-Tsai Wu,et al.  Measuring Pulse Wave Velocity Using ECG and Photoplethysmography , 2011, Journal of Medical Systems.

[5]  A Murray,et al.  Development of a neural network screening aid for diagnosing lower limb peripheral vascular disease from photoelectric plethysmography pulse waveforms. , 1993, Physiological measurement.

[6]  Wen-Miin Liang,et al.  A Novel Noninvasive Measurement Technique for Analyzing the Pressure Pulse Waveform of the Radial Artery , 2008, IEEE Transactions on Biomedical Engineering.

[7]  R H Fagard,et al.  Effect of age on brachial artery wall properties differs from the aorta and is gender dependent: a population study. , 2000, Hypertension.

[8]  E D Lehmann,et al.  Relation between number of cardiovascular risk factors/events and noninvasive Doppler ultrasound assessments of aortic compliance. , 1998, Hypertension.

[9]  M. Sherebrin,et al.  Frequency analysis of the peripheral pulse wave detected in the finger with a photoplethysmograph , 1990, IEEE Transactions on Biomedical Engineering.

[10]  Norden E. Huang,et al.  Ensemble Empirical Mode Decomposition: a Noise-Assisted Data Analysis Method , 2009, Adv. Data Sci. Adapt. Anal..

[11]  M. Nitzan,et al.  Respiration-induced changes in tissue blood volume distal to occluded artery, measured by photoplethysmography. , 2006, Journal of biomedical optics.

[12]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[13]  P. Chowienczyk,et al.  Contour analysis of the photoplethysmographic pulse measured at the finger , 2006, Journal of hypertension.

[14]  R. Gosling,et al.  Photoplethysmographic assessment of pulse wave reflection: blunted response to endothelium-dependent beta2-adrenergic vasodilation in type II diabetes mellitus. , 1999, Journal of the American College of Cardiology.

[15]  Myoungho Lee,et al.  Adaptive threshold method for the peak detection of photoplethysmographic waveform , 2009, Comput. Biol. Medicine.

[16]  S. S. Shen,et al.  A confidence limit for the empirical mode decomposition and Hilbert spectral analysis , 2003, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[17]  N. Huang,et al.  A new view of nonlinear water waves: the Hilbert spectrum , 1999 .

[18]  D. Menicucci,et al.  Deriving the respiratory sinus arrhythmia from the heartbeat time series using empirical mode decomposition , 2003, q-bio/0310002.

[19]  Men-Tzung Lo,et al.  Nonlinear phase interaction between nonstationary signals: a comparison study of methods based on Hilbert-Huang and Fourier transforms. , 2009, Physical review. E, Statistical, nonlinear, and soft matter physics.

[20]  L. Poston,et al.  Association of pulse waveform characteristics with birth weight in young adults , 2005, Journal of hypertension.

[21]  Hsien-Tsai Wu,et al.  Novel application of parameters in waveform contour analysis for assessing arterial stiffness in aged and atherosclerotic subjects. , 2010, Atherosclerosis.

[22]  P. Chowienczyk,et al.  Determination of age-related increases in large artery stiffness by digital pulse contour analysis. , 2002, Clinical science.

[23]  Partha Pratim Kanjilal,et al.  Analysis and characterization of photo-plethysmographic signal , 2001, IEEE Transactions on Biomedical Engineering.

[24]  Hsien-Tsai Wu,et al.  Assessment of Endothelial Function Using Arterial Pressure Signals , 2011, J. Signal Process. Syst..

[25]  T. Andersson,et al.  Vitamin E restores endothelium dependent vasodilatation in cholesterol fed rabbits: in vivo measurements by photoplethysmography. , 1994, Cardiovascular research.

[26]  Chen Lin,et al.  The Nonlinear and nonstationary Properties in EEG Signals: Probing the Complex Fluctuations by Hilbert-Huang Transform , 2009, Adv. Data Sci. Adapt. Anal..

[27]  M. S. Woolfson,et al.  Application of empirical mode decomposition to heart rate variability analysis , 2001, Medical and Biological Engineering and Computing.