Improved Permutation Entropy for Measuring Complexity of Time Series under Noisy Condition

Measuring complexity of observed time series plays an important role for understanding the characteristics of the system under study. Permutation entropy (PE) is a powerful tool for complexity analysis, but it has some limitations. For example, the amplitude information is discarded; the equalities (i.e., equal values in the analysed signal) are not properly dealt with; and the performance under noisy condition remains to be improved. In this paper, the improved permutation entropy (IPE) is proposed. The presented method combines some advantages of previous modifications of PE. Its effectiveness is validated through both synthetic and experimental analyses. Compared with PE, IPE is capable of detecting spiky features and correctly differentiating heart rate variability (HRV) signals. Moreover, it performs better under noisy condition. Ship classification experiment results demonstrate that IPE achieves 28.66% higher recognition rate than PE at 0dB. Hence, IPE could be used as an alternative of PE for analysing time series under noisy condition.

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Complexity analysis of 24 hours heart rate variability time series

The 26th Annual International Conference of the IEEE Engineering in Medicine and Biology Society ◽

10.1109/iembs.2004.1404105 ◽

2005 ◽

Cited By ~ 5

Author(s):

M. Ferrario ◽

M.G. Signorini ◽

S. Cerutti

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Complexity Analysis

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On the Accuracy of Heart Rate Variability Measures from Undersampled RR Interval Time Series

2016 IEEE Global Communications Conference (GLOBECOM) ◽

10.1109/glocom.2016.7842045 ◽

2016 ◽

Author(s):

Giorgio Quer ◽

Amr Alasaad ◽

Ramesh R. Rao

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Interval Time ◽

Rr Interval ◽

Interval Time Series

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On the fractal nature of heart rate variability in humans: effects of respiratory sinus arrhythmia

AJP Heart and Circulatory Physiology ◽

10.1152/ajpheart.1995.269.2.h480 ◽

1995 ◽

Vol 269 (2) ◽

pp. H480-H486 ◽

Cited By ~ 10

Author(s):

Y. Yamamoto ◽

J. O. Fortrat ◽

R. L. Hughson

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Coarse Graining ◽

Resting Heart Rate ◽

Breathing Frequency ◽

Fractal Nature ◽

Sinus Arrhythmia ◽

Spectral Exponent ◽

Random Fractal

The purpose of the present study was to investigate the basic fractal nature of the variability in resting heart rate (HRV), relative to that in breathing frequency (BFV) and tidal volume (TVV), and to test the hypothesis that fractal HRV is due to the fractal BFV and/or TVV in humans. In addition, the possible fractal nature of respiratory volume curves (RVC) and HRV was observed. In the first study, eight subjects were tested while they sat quietly in a comfortable chair for 60 min. Beat-to-beat R-R intervals, i.e., HRV, and breath-by-breath BFV and TVV were measured. In the second study, six subjects were tested while they were in the supine position for 20-30 min. The RVC was monitored continuously together with HRV. Coarse-graining spectral analysis (Yamamoto, Y., and R. L. Hughson, Physica D 68: 250-264, 1993) was applied to these signals to evaluate the percentage of random fractal components in the time series (%Fractal) and the spectral exponent (beta), which characterizes irregularity of the signals. The estimates of beta were determined for each variable only over the range normally used to evaluate HRV. Values for %Fractal and beta of both BFV and TVV were significantly (P < 0.05) greater than those for HRV. In addition, there was no significant (P > 0.05) correlation between the beta values of HRV relative to either BFV (r = 0.14) or TVV (r = 0.34). RVC showed a smooth oscillation as compared with HRV; %Fractal for RVC (42.3 +/- 21.7%, mean +/- SD) was significantly (P < 0.05) lower than that for HRV (78.5 +/- 4.2%).(ABSTRACT TRUNCATED AT 250 WORDS)

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An application of fractional differintegration to heart rate variability time series

Computer Methods and Programs in Biomedicine ◽

10.1016/j.cmpb.2013.02.009 ◽

2013 ◽

Vol 111 (1) ◽

pp. 33-40 ◽

Cited By ~ 8

Author(s):

Miguel A. García-González ◽

Mireya Fernández-Chimeno ◽

Lluis Capdevila ◽

Eva Parrado ◽

Juan Ramos-Castro

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability

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The Fetal Heart Rate Variability due to vibro-acustic stimulation: a complexity analysis

IFMBE Proceedings - 4th European Conference of the International Federation for Medical and Biological Engineering ◽

10.1007/978-3-540-89208-3_321 ◽

2009 ◽

pp. 1353-1356

Author(s):

Manuela Ferrario ◽

M. G. Signorini ◽

G. Magenes

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Fetal Heart Rate ◽

Fetal Heart ◽

Complexity Analysis ◽

Fetal Heart Rate Variability

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Heart Rate Variability in Intensive Care

Journal of Intensive Care Medicine ◽

10.1177/088506669801300505 ◽

1998 ◽

Vol 13 (5) ◽

pp. 252-265 ◽

Cited By ~ 15

Author(s):

Brahm Goldstein ◽

Timothy G. Buchman

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Intensive Care ◽

Time Series Analysis ◽

Fetal Distress ◽

Heart Beat ◽

Dynamic Interactions ◽

Series Analysis ◽

Injured Patients

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.

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