scholarly journals Time Series Analysis using Embedding Dimension on Heart Rate Variability

2018 ◽  
Vol 145 ◽  
pp. 89-96 ◽  
Author(s):  
Ronakben Bhavsar ◽  
Neil Davey ◽  
Na Helian ◽  
Yi Sun ◽  
Tony Steffert ◽  
...  
1998 ◽  
Vol 13 (5) ◽  
pp. 252-265 ◽  
Author(s):  
Brahm Goldstein ◽  
Timothy G. Buchman

Clinicians have long been aware that the normal oscillations in a heart beat are lost during fetal distress, during the early stages of heart failure, with advanced aging, and with critical illness and injury. However, these oscillations, or variability in heart rate and other cardiovascular signals, have largely been ignored or discounted as variances from the mean or average values. It is becoming increasingly clear that these oscillations reflect the dynamic interactions of many physiologic processes, including neuroautonomic regulation of heart rate and blood pressure. We present a synthesis and review of the current literature concerning heart rate variability with special reference to intensive care. This article describes the background of time series analysis of heart rate variability including time and frequency domain and nonlinear measurements. The implications and potential for time series analysis of variability in cardiovascular signals in clinical diagnosis and management of critically ill and injured patients are discussed.


2015 ◽  
Author(s):  
Erika González ◽  
Jehú López ◽  
Mathieu Hautefeuille ◽  
Víctor Velázquez ◽  
Jésica Del Moral

Author(s):  
M.P. Hanias ◽  
G. S. Tombras

Simple chaotic electronics circuits as diode resonator circuits, Resistor-Inductor-LED optoelectronic chaotic circuits, and Single Transistor chaotic circuits can be used as transmitters and receivers for chaotic cryptosystems. In these circuits we can change and investigate the influence of various circuit parameters to the complexity of the so generated strange attractors. Time series analysis is performed following Grassberger and Procaccia’s method while invariant parameters as correlation, and minimum embedding dimension are respectively calculated. The Kolmogorov entropy is also calculated and the RLT circuits in a critical state are examined.


1993 ◽  
Vol 29 (Supplement) ◽  
pp. 410-411
Author(s):  
Kiyoko YOKOYAMA ◽  
Masanori MOYOSHI ◽  
Yosaku WATANABE ◽  
Kazuyuki TAKATA

1990 ◽  
Vol 258 (3) ◽  
pp. H896-H902 ◽  
Author(s):  
G. E. Billman ◽  
J. P. Dujardin

A time-series analysis of heart rate variability was evaluated as a marker of cardiac vagal tone using well-characterized autonomic interventions. Heart period (R-R interval) was recorded in 14 mongrel dogs from which the amplitude of the respiratory sinus arrhythmia (0.24-1.04 Hz) was determined. Exercise elicited significant (P less than 0.01) reductions in the index of vagal tone (control 6.3 +/- 0.3 ln ms2 vs. exercise 2.4 +/- 0.4 ln ms2) that were accompanied by significant (P less than 0.01) increases in heart rate (control 123.1 +/- 5 vs. exercise 201.0 +/- 7.7 beats/min). The vagal tone index remained greater than 0 throughout exercise. After propranolol HCl pretreatment, the vagal tone index rapidly decreased toward zero (control 6.2 +/- 0.5; exercise 0.7 +/- 0.3 ln ms2), despite significantly lower increases in heart rate (control 109.3 +/- 4.2; exercise 178.0 +/- 7.6 beats/min). Atropine given during exercise evoked significantly greater increases in heart rate in the control (+48.7 +/- 7.9 beats/min) vs. propranolol (+14.2 +/- 6.7 beats/min) conditions. These data suggest that 1) high levels of cardiac vagal tone remain during exercise; 2) vagal withdrawal is largely responsible for the heart rate increase after beta-adrenergic receptor blockade; and 3) time-series analysis of the R-R interval can provide a dynamic and noninvasive index of cardiac vagal tone.


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