scholarly journals DETRENDED FLUCTUATION ANALYSES OF SHORT-TERM HEART RATE VARIABILITY IN SURGICAL INTENSIVE CARE UNITS

2006 ◽  
Vol 18 (02) ◽  
pp. 67-72 ◽  
Author(s):  
RONG-GUAN YEH ◽  
JIANN-SHING SHIEH ◽  
YIN-YI HAN ◽  
YU-JUNG WANG ◽  
SHIH-CHUN TSENG
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
White Noise ◽  
Short Term ◽  
Surgical Intensive Care ◽  
Ecg Signals ◽  
Different Types ◽  
Group A ◽  
Detrended Fluctuation

We examine the dynamics of complex physiologic fluctuations using methods developed very recently in statistical physics. The method based on detrended fluctuation analysis (DFA) has been used to investigate the profile of different types of physiologic states under long term (i.e., 24 hr) analysis of heart rate variability (HRV). In this paper, this method to investigate the output of central physiologic control system under short term (i.e., 1 hr) of HRV in surgical intensive care units (SICU). Electrocardiograph (ECG) signals lasting around 1 hr were collected from ten college student volunteers as group A. Ten computes-generates white noise signals as group B also provided ECG signals lasting around 1 hr. Finally, seventeen patients representing 37 cases undergoing different types of neurosurgery were studied as group C. From this group, 25 cases were selected from 15 patients with brain injury and 12 cases were selected from 2 patients with septicemia. Group A and B were used as high and low limits of baseline for comparison with pathologic states in the SICU. The a values of DFA of groups A, B, and C were 0.958 ± 0.034, 0.521 ± 0.010, and 0.815 ± 0.183, respectively. It was found that the α value of patients in the SICU was significantly lower (P < 0.05) than that of healthy volunteers and significantly higher (P < 0.05) than white noise signals. Hence, it can be concluded that α values based on the DFA statistical concept can clearly distinguish pathologic states in SICU patients from the healthy group and from white noise signals.

NONRANDOMNESS INDEX APPLIED FOR HEART RATE VARIABILITY IN SURGICAL INTENSIVE CARE UNITS USING FREQUENCY AND RANK ORDER STATISTICS

2007 ◽  
Vol 19 (05) ◽  
pp. 303-311 ◽  
Author(s):  
Rong-Guan Yeh ◽  
Yin-Yi Han ◽  
Jiann-Shing Shieh ◽  
Yu-Jung Wang ◽  
Shih-Chun Tseng ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Intensive Care ◽  
Healthy Volunteers ◽  
Order Statistics ◽  
Intensive Care Units ◽  
Rank Order ◽  
Surgical Intensive Care ◽  
Ecg Signals ◽  
Rank Order Statistics

The complexity of physiologic signals may carry hidden dynamical structures that are related to their underlying mechanisms. Based on rank order statistics of symbolic sequences, we applied this method to heart rate variability (HRV) in surgical intensive care units (SICU) in order to determine a nonrandomness index to help doctors diagnose patients more rapidly in a SICU in the future. Twenty one patients with 47 cases undergoing different types of neurosurgery were studied as group A. From this group, electrocardiograph (ECG) signals were collected. They lasting around 60 min for 29 cases selected from 16 patients with brain injury, 12 cases selected from 2 patients with septicemia, and 6 cases selected from 3 patients with mechanical ventilator. Ten college student volunteers as group B also provided ECG signals lasting around 60 min. Finally, ten randomized surrogate signals generated from a computer as group C were used as baseline for comparison with healthy volunteers and pathologic states in the SICU. The nonrandomness indexes of groups A, B, and C were 0.160 ± 0.100, 0.237 ± 0.051, and 0.030 ± 0.003, respectively. It was found that this index of patients in the SICU was significantly lower (P < 0.05) than healthy volunteers and significantly higher (P < 0.05) than randomized surrogate signals. These results demonstrate that the nonrandomness index based on rank order statistics concept can clearly distinguish pathologic states in SICU from the healthy group and the randomized surrogate signals.

Detrended fluctuation analysis of short-term heart rate variability in late pregnant women

2009 ◽  
Vol 150 (1-2) ◽  
pp. 122-126 ◽  
Author(s):  
Rong-Guan Yeh ◽  
Jiann-Shing Shieh ◽  
Gau-Yang Chen ◽  
Cheng-Deng Kuo
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Pregnant Women ◽  
Detrended Fluctuation Analysis ◽  
Fluctuation Analysis ◽  
Short Term ◽  
Detrended Fluctuation

scholarly journals Multiscale Distribution Entropy Analysis of Short-Term Heart Rate Variability

Entropy ◽  
2018 ◽  
Vol 20 (12) ◽  
pp. 952 ◽  
Author(s):  
Dae-Young Lee ◽  
Young-Seok Choi
Keyword(s):  
Heart Rate ◽  
Time Series ◽  
Heart Rate Variability ◽  
Coarse Grained ◽  
Multiscale Entropy ◽  
Short Term ◽  
Ecg Signals ◽  
Improved Stability ◽  
Moving Averaging ◽  
Electrocardiogram Ecg

Electrocardiogram (ECG) signal has been commonly used to analyze the complexity of heart rate variability (HRV). For this, various entropy methods have been considerably of interest. The multiscale entropy (MSE) method, which makes use of the sample entropy (SampEn) calculation of coarse-grained time series, has attracted attention for analysis of HRV. However, the SampEn computation may fail to be defined when the length of a time series is not enough long. Recently, distribution entropy (DistEn) with improved stability for a short-term time series has been proposed. Here, we propose a novel multiscale DistEn (MDE) for analysis of the complexity of short-term HRV by utilizing a moving-averaging multiscale process and the DistEn computation of each moving-averaged time series. Thus, it provides an improved stability of entropy evaluation for short-term HRV extracted from ECG. To verify the performance of MDE, we employ the analysis of synthetic signals and confirm the superiority of MDE over MSE. Then, we evaluate the complexity of short-term HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The experimental results exhibit that MDE is capable of quantifying the decreased complexity of HRV with aging and CHF disease with short-term HRV time series.

scholarly journals INVESTIGATION OF HEART RATE VARIABILITY BY STATISTICAL METHODS AND DETRENDED FLUCTUATION ANALYSIS

2019 ◽  
Vol 7 ◽  
Author(s):  
Galya Nikolova Georgieva-Tsaneva
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Statistical Methods ◽  
Detrended Fluctuation Analysis ◽  
Age Groups ◽  
Research Problem ◽  
Fluctuation Analysis ◽  
Short Term ◽  
Detrended Fluctuation

The physiological signals that are recorded from different parts of the human body have a non-stationary nature and the tracking of their dynamics is an interesting research problem. This report examines Heart Rate Variability through the use of statistical methods of analysis that are traditionally used to study the functionality of the heart and via Detrended Fluctuation Analysis. The use of the technique of Detrended Fluctuation Analysis allows the investigation of short-term and long-term correlations in non-stationary Heart Rate Variability series. A study has been made of the changes in the functioning of the human heart, depending on the age. The study encompasses healthy individuals in three different age groups. The analysis of the obtained results shows a change in the correlated behavior of the investigated signals with an increase in age.

scholarly journals Differential effects of the blood pressure state on pulse rate variability and heart rate variability in critically ill patients

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Elisa Mejía-Mejía ◽  
James M. May ◽  
Mohamed Elgendi ◽  
Panayiotis A. Kyriacou
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Pulse Rate ◽  
Pulse Transit Time ◽  
Pulse Rate Variability ◽  
Physiological Factors ◽  
Ecg Signals ◽  
Non Invasive ◽  
Electrocardiogram Ecg

AbstractHeart rate variability (HRV) utilizes the electrocardiogram (ECG) and has been widely studied as a non-invasive indicator of cardiac autonomic activity. Pulse rate variability (PRV) utilizes photoplethysmography (PPG) and recently has been used as a surrogate for HRV. Several studies have found that PRV is not entirely valid as an estimation of HRV and that several physiological factors, including the pulse transit time (PTT) and blood pressure (BP) changes, may affect PRV differently than HRV. This study aimed to assess the relationship between PRV and HRV under different BP states: hypotension, normotension, and hypertension. Using the MIMIC III database, 5 min segments of PPG and ECG signals were used to extract PRV and HRV, respectively. Several time-domain, frequency-domain, and nonlinear indices were obtained from these signals. Bland–Altman analysis, correlation analysis, and Friedman rank sum tests were used to compare HRV and PRV in each state, and PRV and HRV indices were compared among BP states using Kruskal–Wallis tests. The findings indicated that there were differences between PRV and HRV, especially in short-term and nonlinear indices, and although PRV and HRV were altered in a similar manner when there was a change in BP, PRV seemed to be more sensitive to these changes.

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