A bounded random process model and its application in heart rate variability analysis

A bounded random process (BRP) model was developed in this paper to interpret why approximate entropy (ApEn) can not describe the stochastic characteristic of Brownian motion time series correctly. Low ApEn value, generally implying presence of determinacy, also existed in Brownian motion series, a stochastic process. The BRP model investigated this phenomenon through quantifying the relationship between ApEn and a parameter of BRP model. BRP model was then applied to analyze electrocardiograph (ECG) time series from 60 healthy subjects and 60 myocardial infarction (MI) patients. ApEn of the healthy group had a close relationship with the parameter of BRP model, while this relationship could not be found in MI patient group. Grounded on combination of BRP and ApEn, a classifier was designed to assist to diagnose the MI patients. ROC curve and classification figures verified the classifier.   Key words: Bounded random process, approximate entropy, Brownian motion, heart rate variability.

[1]  S M Pincus,et al.  Approximate entropy as a measure of system complexity. , 1991, Proceedings of the National Academy of Sciences of the United States of America.

[2]  C. Peng,et al.  What is physiologic complexity and how does it change with aging and disease? , 2002, Neurobiology of Aging.

[3]  A L Goldberger,et al.  Physiological time-series analysis: what does regularity quantify? , 1994, The American journal of physiology.

[4]  M L Johnson,et al.  Impact of pulsatility on the ensemble orderliness (approximate entropy) of neurohormone secretion. , 2001, American journal of physiology. Regulatory, integrative and comparative physiology.

[5]  X. Ning,et al.  Estimation of ApEn for short-term HFECG based on multi-resolution analysis , 2007 .

[6]  M. Hénon.  A two-dimensional mapping with a strange attractor , 1976 .

[7]  Metin Akay,et al.  Approximate Entropy and its Application in Biosignal Analysis , 2000 .

[9]  Roberto Hornero,et al.  Interpretation of approximate entropy: analysis of intracranial pressure approximate entropy during acute intracranial hypertension , 2005, IEEE Transactions on Biomedical Engineering.

[10]  B. Celler,et al.  Selection of optimal parameters for ECG diagnostic classification , 1997, Computers in Cardiology 1997.

[11]  P. de Chazal,et al.  Improving ECG diagnostic classification by combining multiple neural networks , 1997, Computers in Cardiology 1997.

[12]  Carolyn McGregor,et al.  Heart disease classification through HRV analysis using Parallel Cascade Identification and Fast Orthogonal Search , 2010, 2010 IEEE International Workshop on Medical Measurements and Applications.

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