Morphology Variability Analysis of Wrist Pulse Waveform for Assessment of Arteriosclerosis Status

In this paper, approximate entropy (ApEn) is applied to study the variability of pulse waveform for assessing coronary arteriosclerosis status. Having analyzed the wrist pulse waveforms taken from both normal subjects and the patients suffering from coronary arteriosclerosis (CA) disorders, we find that pulse morphology variability (PMV) is more efficient than pulse interval variability (PIV) in assessing the conditions of human coronary artery. Usually, the PMVs of the healthy are higher than those of the patients with CA diseases, and the PMVs of patients with CA diseases have more high frequency components than those of the healthy subjects. That is to say, the CA disease also has influence on vascular tone. The effect of changes in cardiac performance due to CA disease can be reflected through the PMV. The experiment demonstrates that the specificity and sensitivity of the PMV’s spectral energy ratio for clinical diagnosis of cardiovascular system is 80% and 97%, respectively.

[1]  Remzi Seker,et al.  Validity Test for a Set of Nonlinear Measures for Short Data Length with Reference to Short-Term Heart Rate Variability Signal , 2000, J. Syst. Integr..

[2]  L. Brush,et al.  McDonaldʼs Blood Flow in Arteries , 1991 .

[3]  David Zhang,et al.  Approximate entropy based pulse variability analysis , 2003, 16th IEEE Symposium Computer-Based Medical Systems, 2003. Proceedings..

[4]  J. Cohn,et al.  Age-related abnormalities in arterial compliance identified by pressure pulse contour analysis: aging and arterial compliance. , 1999, Hypertension.

[5]  S H Yoon,et al.  Clinical study of objective pulse diagnosis. , 1986, The American journal of Chinese medicine.

[6]  H. T. Nagle,et al.  A comparison of the noise sensitivity of nine QRS detection algorithms , 1990, IEEE Transactions on Biomedical Engineering.

[7]  Lisheng Xu,et al.  Arrhythmic Pulses Detection Using Lempel-Ziv Complexity Analysis , 2006, EURASIP J. Adv. Signal Process..

[8]  David Zhang,et al.  Baseline wander correction in pulse waveforms using wavelet-based cascaded adaptive filter , 2007, Comput. Biol. Medicine.

[9]  C. Hayward,et al.  Noninvasive determination of age-related changes in the human arterial pulse. , 1989, Circulation.

[10]  D. Yeshurun,et al.  Assessment of cardiovascular reactivity by fractal and recurrence quantification analysis of heart rate and pulse transit time , 2003, Journal of Human Hypertension.

[11]  W. B. Murray,et al.  The peripheral pulse wave: Information overlooked , 1996, Journal of clinical monitoring.

[12]  Steven M. Pincus,et al.  Approximate entropy: Statistical properties and applications , 1992 .

[13]  A. Wolf,et al.  Determining Lyapunov exponents from a time series , 1985 .

[14]  Westgate Road,et al.  Photoplethysmography and its application in clinical physiological measurement , 2007 .

[15]  Ling Y. Wei,et al.  Spectrum Analysis of Human Pulse , 1983, IEEE Transactions on Biomedical Engineering.

[16]  Yoshiharu Yonezawa,et al.  A wrist-mounted activity and pulse recording system , 1999, Proceedings of the First Joint BMES/EMBS Conference. 1999 IEEE Engineering in Medicine and Biology 21st Annual Conference and the 1999 Annual Fall Meeting of the Biomedical Engineering Society (Cat. N.

[17]  S. Pincus Approximate entropy (ApEn) as a complexity measure. , 1995, Chaos.

[18]  Steven M. Pincus,et al.  Quantification of hormone pulsatility via an approximate entropy algorithm. , 1992, The American journal of physiology.

[19]  M. Malik Heart Rate Variability , 1996, Clinical cardiology.

[20]  W A Lu,et al.  Pulse analysis of patients with severe liver problems. Studying pulse spectrums to determine the effects on other organs. , 1999, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[21]  G. Breithardt,et al.  Heart rate variability: standards of measurement, physiological interpretation and clinical use. Task Force of the European Society of Cardiology and the North American Society of Pacing and Electrophysiology. , 1996 .

[22]  Y Z Yoon,et al.  Pulse type classification by varying contact pressure. , 2000, IEEE engineering in medicine and biology magazine : the quarterly magazine of the Engineering in Medicine & Biology Society.

[23]  A. Malliani,et al.  Heart rate variability. Standards of measurement, physiological interpretation, and clinical use , 1996 .

[24]  G. Parati,et al.  Spectral analysis of blood pressure and heart rate variability in evaluating cardiovascular regulation. A critical appraisal. , 1995, Hypertension.

[25]  W. Nichols McDonald's Blood Flow in Arteries , 1996 .

[26]  Michael A. Navakatikyan,et al.  A real-time algorithm for the quantification of blood pressure waveforms , 2002, IEEE Transactions on Biomedical Engineering.

[27]  D. Webb,et al.  Noninvasive assessment of arterial stiffness and risk of atherosclerotic events. , 2003, Arteriosclerosis, thrombosis, and vascular biology.

[28]  B.G. Celler,et al.  Extraction of photoplethysmographic waveform variability by lowpass filtering , 2005, 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference.

[29]  Junyoung Lee The Study on the Intellectual Analysis Algorithm for Oriental Pulse Parameters , 2007, Journal of Medical Systems.

[30]  Takayuki Kageyama,et al.  Accuracy of Pulse Rate Variability Parameters Obtained from Finger Plethysmogram: A Comparison with Heart Rate Variability Parameters Obtained from ECG , 1997 .

[31]  M. O'Rourke,et al.  Pulse wave analysis. , 2001, British journal of clinical pharmacology.