COMPLEXITY AND INFORMATION-BASED ANALYSIS OF THE HEART RATE VARIABILITY (HRV) WHILE SITTING, HAND BIKING, WALKING, AND RUNNING

Fractals ◽

10.1142/s0218348x21502017 ◽

2021 ◽

pp. 2150201

Author(s):

HAMIDREZA NAMAZI

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Fractal Dimension ◽

Information Content ◽

Shannon Entropy ◽

Human Activities ◽

Complex Structure ◽

Sample Entropy ◽

Heart Activity

Analysis of the variations of heart activity during different human activities is an important area of research in sport sciences. Therefore, in this paper, we evaluated the variations of heart activity for 23 subjects while sitting, hand biking, walking, and running. Since the obtained R-R time series (as the indicator of heart rate variability (HRV)) has a complex structure that contains information, we employed fractal dimension, sample entropy, and Shannon entropy for our analysis. According to the results, doing a harder activity causes a more significant alteration in the complexity and information content of HRV. The results of statistical analyses also verified the obtained results. Similar investigations can be conducted in case of other activities to evaluate the variations in heart activity in different conditions.

Download Full-text

Complexity-Based Decoding of the Coupling Among Heart Rate Variability (HRV) and Walking Path

Frontiers in Physiology ◽

10.3389/fphys.2020.602027 ◽

2020 ◽

Vol 11 ◽

Author(s):

Shahul Mujib Kamal ◽

Mohammad Hossein Babini ◽

Ondrej Krejcar ◽

Hamidreza Namazi

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Fractal Dimension ◽

Daily Life ◽

Fractal Theory ◽

Sample Entropy ◽

Physiological Signals ◽

Everyday Activity ◽

First Time

Walking is an everyday activity in our daily life. Because walking affects heart rate variability, in this research, for the first time, we analyzed the coupling among the alterations of the complexity of walking paths and heart rate. We benefited from the fractal theory and sample entropy to evaluate the influence of the complexity of paths on the complexity of heart rate variability (HRV) during walking. We calculated the fractal exponent and sample entropy of the R-R time series for nine participants who walked on four paths with various complexities. The findings showed a strong coupling among the alterations of fractal dimension (an indicator of complexity) of HRV and the walking paths. Besides, the result of the analysis of sample entropy also verified the obtained results from the fractal analysis. In further studies, we can analyze the coupling among the alterations of the complexities of other physiological signals and walking paths.

Download Full-text

Analysis of Pregnancy Development by Complexity and Information-Based Analysis of Fetal Phonocardiogram (PCG) Signals

Fluctuation and Noise Letters ◽

10.1142/s0219477521500280 ◽

2020 ◽

pp. 2150028

Author(s):

Hamidreza Namazi ◽

Ondrej Krejcar

Keyword(s):

Heart Rate ◽

Fractal Dimension ◽

Information Content ◽

Shannon Entropy ◽

Gestational Age ◽

Approximate Entropy ◽

Sample Entropy ◽

Biomedical Signals ◽

Pcg Signals

One of the crucial areas of pregnancy research is to analyze the pregnancy development. For this purpose, scientists analyze the different conditions of fetuses to understand their development. In this paper, we conducted complexity and information-based analyses on Phonocardiogram (PCG) signals to investigate pregnancy development. We calculated the fractal dimension, approximate entropy, and sample entropy as the measures of complexity and the Shannon entropy as the measure of the information content of signals for 24 fetuses in four ranges of gestational weeks. Based on the obtained results, increasing the gestational age of fetuses is reflected on the increment of the complexity of their PCG signals. We also observed similar findings in the case of the information content of PCG signals. Among all calculated measures, the fractal dimension of PCG signals showed significant variations among different gestational weeks. The method of analysis can be used to evaluate the alterations of other biomedical signals of fetuses (e.g., heart rate) to investigate their development.

Download Full-text

AGE-BASED ANALYSIS OF HEART RATE VARIABILITY (HRV) FOR PATIENTS WITH CONGESTIVE HEART FAILURE

Fractals ◽

10.1142/s0218348x21501358 ◽

2021 ◽

pp. 2150135

Author(s):

HAMIDREZA NAMAZI ◽

DUMITRU BALEANU ◽

ONDREJ KREJCAR

Keyword(s):

Heart Failure ◽

Heart Rate ◽

Congestive Heart Failure ◽

Heart Rate Variability ◽

Fractal Dimension ◽

Age Groups ◽

Approximate Entropy ◽

Sample Entropy ◽

Heart Activity ◽

Complexity Measures

It is known that heart activity changes during aging. In this paper, we evaluated alterations of heart activity from the complexity point of view. We analyzed the variations of heart rate of patients with congestive heart failure that are categorized into four different age groups, namely 30–39, 50–59, 60–69, and 70–79 years old. For this purpose, we employed three complexity measures that include fractal dimension, sample entropy, and approximate entropy. The results showed that the trend of increment of subjects’ age is reflected in the trend of increment of the complexity of heart rate variability (HRV) since the values of fractal dimension, approximate entropy, and sample entropy increase as subjects get older. The analysis of the complexity of other physiological signals can be further considered to investigate the variations of activity of other organs due to aging.

Download Full-text

A New Tool for Nonlinear Dynamical Analysis of Heart Rate Variability

Zeitschrift für Naturforschung A ◽

10.1515/zna-1998-3-403 ◽

1998 ◽

Vol 53 (3-4) ◽

pp. 112-116 ◽

Cited By ~ 3

Author(s):

A. Posiewnik ◽

J. Da̢bkowski

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Qualitative Assessment ◽

Dynamical Analysis ◽

Heart Activity ◽

Time Intervals ◽

Rr Intervals ◽

Nonlinear Dynamical ◽

Nonlinear Dynamical Analysis

Abstract In this paper we analyse the sequences of the time intervals between heart-beats-the RR intervals-by means of AIP (artificial insymmetration patterns) diagrams.The sequences were produced by artificial heartbeat sequences generated numerically and compared with sequences obtained from real heart activity.We hope that the AIP diagrams method will prove useful for a rapid qualitative assessment of dynamics from nonlinear time series, and that it is able to distinguish various types of heart dynamics (regular and pathological), while other diagnostical methods fail.

Download Full-text

Information-Based Analysis of the Coupling Between the Alterations of Heart and Brain Activities in Response to Auditory Stimuli

Fluctuation and Noise Letters ◽

10.1142/s0219477521500498 ◽

2021 ◽

pp. 2150049

Author(s):

Hamidreza Namazi ◽

Tisara Kumarasinghe ◽

Ondrej Krejcar

Keyword(s):

Heart Rate ◽

Time Series ◽

Heart Rate Variability ◽

Shannon Entropy ◽

Auditory Stimuli ◽

Eeg Signals ◽

Information Contents ◽

The Difference ◽

The Brain

In this work, we investigated the coupling among the activities of the brain and heart versus the changes in auditory stimuli using information-based analysis. Three music were selected based on the difference in their complexity. We applied these auditory stimuli on 11 subjects, and accordingly, computed and compared the Shannon entropy of electroencephalography (EEG) signals and heart rate variability (R–R time series). The results demonstrated a correlation among the alterations of the information contents of EEG signals and R–R time series. This finding shows the coupling between the activities of the brain and heart. This analysis could be expanded to analyze the activities of other organs versus the brain’s reaction in various conditions.

Download Full-text

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

Download Full-text

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)

Download Full-text