Computer-based analysis of cardiac state using entropies, recurrence plots and Poincare geometry

被引：22

作者：

Department of Electronics and Computer Engineering, Ngee Ann Polytechnic, Singapore 59001, Singapore ^{[1
]}

不详 ^{[2
]}

机构：

[1] Department of Electronics and Computer Engineering, Ngee Ann Polytechnic

[2] School of Engineering Systems, Queensland University of Technology

来源：

J. Med. Eng. Technol. | 2008年 / 4卷 / 263-272期

关键词：

Approximate entropy; Heart rate; Poincare plot; Recurrence plot; Sample entropy;

D O I：

10.1080/03091900600863794

中图分类号：

学科分类号：

摘要：

Heart rate variability refers to the regulation of the sinoatrial node, the natural pacemaker of the heart by the sympathetic and parasympathetic branches of the autonomic nervous system. Heart rate variability is important because it provides a window to observe the heart's ability to respond to normal regulatory impulses that affect its rhythm. A computer-based intelligent system for analysis of cardiac states is very useful in diagnostics and disease management. Parameters are extracted from the heart rate signals and analysed using computers for diagnostics. This paper describes the analysis of normal and seven types of cardiac abnormal signals using approximate entropy (ApEn), sample entropy (SampEn), recurrence plots and Poincare plot patterns. Ranges of these parameters for various cardiac abnormalities are presented with an accuracy of more than 95%. Among the two entropies, ApEn showed better performance for all the cardiac abnormalities. Typical Poincare and recurrence plots are shown for various cardiac abnormalities. © 2008 Informa UK Ltd.

引用

页码：263 / 272

页数：9

共 29 条

[1]

Saul J.P., Beat to beat variation of heart rate reflects modulations of cardiac autonomic outflow, News Physiological Science, 5, pp. 32-37, (1990)

[2]

Weissman M.W., Markowitz J.S., Ouelette R., Greenwald S., Hahn J.P., Panic disorder and cardiovascular/cerebrovascular problems, American Journal of Psychiatry, 147, pp. 1504-1507, (1990)

[3]

Kaplan D.T., Cohen J.R., Searching for Chaos in fibrillation, Annals of the New York Academy of Sciences, 591, pp. 367-374, (1991)

[4]

Cohen M.E., Hudson D.L., Deedwania P.C., Applying continuous chaotic modeling to cardiac signal analysis, IEEE Engineering in Medicine and Biology, 15, pp. 97-102, (1986)

[5]

Narayana D.D., Krishnan S.M., Application of phase space techniques to the analysis of cardiac signals, Proceedings of IEEE EMBS Conference, (1999)

[6]

Owis M.I., Abou-Zied A.H., Youssef A.-B.M., Kadah Y.M., Study of features on nonlinear dynamical modeling in ECG arrhythmia detection and classification, IEEE transactions on Biomedical Engineering, 49, pp. 733-736, (2002)

[7]

Yerangani V.K., Sobolewski E., Jampala V.C., Yeragani S., Igel G., Fractal dimension and approximate entropy of heart rate period and heart rate: Awake versus sleep differences and methodological issues, Clinical Science, 95, pp. 295-301, (1998)

[8]

Brennan M., Palaniswami M., Kamen P., Do existing measures of Poincare plot geometry reflect nonlinear features of heart rate variability?, IEEE Transactions on Biomedical Engineering, 48, pp. 1342-1347, (2001)

[9]

Eckmann J.P., Kamphorst S.O., Ruelle D., Recurrence plots of dynamical systems, Europhysics Letters, 4, pp. 973-977, (1987)

[10]

Kurths J.U., Schwarz C.P., Sonett P.U., Testing nonlinearity in radio carbon data, Nonlinear Processes in Geophysics, 1, pp. 72-75, (1994)

← 1 2 3 →