A characterization of HRV's nonlinear hidden dynamics by means of Markov models
| Autor: | Gustavo Deco, Celio Gremigni, Rosaria Silipo, Rossano Vergassola |
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| Rok vydání: | 1999 |
| Předmět: |
Male
Time Factors Myocardial Ischemia Biomedical Engineering Markov process Higher-order statistics Markov model Angina Pectoris Electrocardiography symbols.namesake Heart Rate Risk Factors Control theory Humans Heart rate variability Aged Mathematics Fourier Analysis Markov chain business.industry Models Cardiovascular Pattern recognition Middle Aged Markov Chains Circadian Rhythm Autonomic nervous system Nonlinear system Nonlinear Dynamics Data Interpretation Statistical Hidden variable theory Electrocardiography Ambulatory Exercise Test Tachycardia Ventricular symbols Female Artificial intelligence business Algorithms |
| Zdroj: | IEEE Transactions on Biomedical Engineering. 46:978-986 |
| ISSN: | 0018-9294 |
| DOI: | 10.1109/10.775408 |
| Popis: | A study of the 24-h heart rate variability's (HRV) hidden dynamic is performed hour by hour, in order to investigate the evolution of the nonlinear structure of the underlying nervous system. A hierarchy of null hypotheses of nonlinear Markov models with increasing order n is tested against the hidden dynamic of the HRV time series. The minimum accepted Markov order supplies information about the nonlinearity of the HRV's hidden dynamic and consequently of the underlying nervous system. The Markov model with minimum order is detected for each hour of the RR time series extracted from seven 24-h electrocardiogram records of patients in different patho-physiological conditions, some including ventricular tachycardia episodes. Heart rate, pNN30, and LF/HF index plots are reported to serve as a reference for the description of the patient's cardiovascular frame during each examined hour. The minimum Markov order shows to be a promising index for quantifying the average nonlinearity of the autonomic nervous system's activity. |
| Databáze: | OpenAIRE |
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