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CLC number: TN911.72; R318.04
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Received: 2006-12-11
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Some nonlinear parameters of PP intervals of pulse main peaks
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HAN Qing-peng, WANG Ping. Some nonlinear parameters of PP intervals of pulse main peaks[J]. Journal of Zhejiang University Science A, 2007, 8(8): 1256-1262.
@article{title="Some nonlinear parameters of PP intervals of pulse main peaks",
author="HAN Qing-peng, WANG Ping",
journal="Journal of Zhejiang University Science A",
volume="8",
number="8",
pages="1256-1262",
year="2007",
publisher="Zhejiang University Press & Springer",
doi="10.1631/jzus.2007.A1256"
}
%0 Journal Article
%T Some nonlinear parameters of PP intervals of pulse main peaks
%A HAN Qing-peng
%A WANG Ping
%J Journal of Zhejiang University SCIENCE A
%V 8
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%@ 1673-565X
%D 2007
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.2007.A1256
TY - JOUR
T1 - Some nonlinear parameters of PP intervals of pulse main peaks
A1 - HAN Qing-peng
A1 - WANG Ping
J0 - Journal of Zhejiang University Science A
VL - 8
IS - 8
SP - 1256
EP - 1262
%@ 1673-565X
Y1 - 2007
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.2007.A1256
Abstract: The PP intervals of pulse main peaks from healthy and unhealthy people (arrhythmia) have different nonlinear characteristics. In this paper, the extraction of PP intervals of pulse main peaks is achieved by picking up P peaks of pulse wave with wavelet transform. Furthermore, several nonlinear parameters (correlative dimensions, maximum lyapunov exponents, complexity and approximate entropy) of the PP intervals of pulse main peaks extracted from normal and unhealthy pulse signals are calculated, with the results showing that these nonlinear parameters calculated from the main wave interval signals are helpful for analyzing human’s health state and diagnosing heart diseases.
[1] Cao, L., 1997. Practical method for determining the minimum embedding dimension of a scalar time series. Physica D, 110:43-50.
[2] Chiu, C.C., Yeh, S.J., Liu, Y.L., Chen, C.H., 2000. Self-organizing arterial pressure pulse classification using neural networks: theoretical considerations and clinical applicability. Computers in Biol. & Med., 30:71-88.
[3] Chiu, C.C., Yeh, S.J., Liu, Y.L., Liau, B.Y., 2004. Nonlinear dynamic analysis of blood pressure variability in diabetics with autonomic neuropathy using approximate entropy. Clinical Autonomic Research, 14:343-348.
[4] Drinnan, M.J., Allen, J., Murray, A., 2001. Relation between heart rate and pulse transit time during paced respiration. Physiol. Meas., 22:425-432.
[5] Ferri, R., Pettinato, S., 1999. Correlation dimension of EEG slow-wave activity during sleep in narcoleptic patients under bed rest conditions. Int. J. Psychophys., 43:34-37.
[6] Kim, D.J., Jeong, J., 2000. An estimation of the first positive Lyapunov exponent of the EEG in patients with schizophrenia. Psychiatry Research: Neuroimaging Section, 98:177-189.
[7] Klonowski, W., 2000. Signal and image analysis using chaos theory and fractal geometry. Machine Graphics Vision, 9:403-431.
[8] Pincus, S.M., 1991. Approximate entropy as a measure of system complexity. PNAS, 88:2297-2301.
[9] Pincus, S.M., 1995. Approximate entropy (ApEn) as a complexity measure. Chaos, 5:110-117.
[10] Sita, G., Ramakrishnan, A.G., 2000. Wavelet domain nonlinear filtering for evoked potential signal enhancement. Computers and Biomedical Research, 33:431-446.
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