scholarly journals Longitudinal Changes and Recovery in Heart Rate Variability of Young Healthy Subjects When Exposure to a Hypobaric Hypoxic Environment

2022 ◽  
Vol 12 ◽  
Author(s):  
Chenbin Ma ◽  
Haoran Xu ◽  
Muyang Yan ◽  
Jie Huang ◽  
Wei Yan ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Time Domain ◽  
Healthy Subjects ◽  
Multiscale Entropy ◽  
Healthy Population ◽  
Poincaré Plot ◽  
Longitudinal Changes ◽  
Hypoxic Environment

Background: The autonomic nervous system (ANS) is crucial for acclimatization. Investigating the responses of acute exposure to a hypoxic environment may provide some knowledge of the cardiopulmonary system’s adjustment mechanism.Objective: The present study investigates the longitudinal changes and recovery in heart rate variability (HRV) in a young healthy population when exposed to a simulated plateau environment.Methods: The study followed a strict experimental paradigm in which physiological signals were collected from 33 healthy college students (26 ± 2 years, 171 cm ± 7 cm, 64 ± 11 kg) using a medical-grade wearable device. The subjects were asked to sit in normoxic (approximately 101 kPa) and hypoxic (4,000 m above sea level, about 62 kPa) environments. The whole experimental process was divided into four stable resting measurement segments in chronological order to analyze the longitudinal changes of physical stress and recovery phases. Seventy-six time-domain, frequency-domain, and non-linear indicators characterizing rhythm variability were analyzed in the four groups.Results: Compared to normobaric normoxia, participants in hypobaric hypoxia had significantly lower HRV time-domain metrics, such as RMSSD, MeanNN, and MedianNN (p < 0.01), substantially higher frequency domain metrics such as LF/HF ratio (p < 0.05), significantly lower Poincaré plot parameters such as SD1/SD2 ratio and other Poincaré plot parameters are reduced considerably (p < 0.01), and Refined Composite Multi-Scale Entropy (RCMSE) curves are reduced significantly (p < 0.01).Conclusion: The present study shows that elevated heart rates, sympathetic activation, and reduced overall complexity were observed in healthy subjects exposed to a hypobaric and hypoxic environment. Moreover, the results indicated that Multiscale Entropy (MSE) analysis of RR interval series could characterize the degree of minor physiological changes. This novel index of HRV can better explain changes in the human ANS.

scholarly journals Sex Differences in Time-Domain and Frequency-Domain Heart Rate Variability Measures of Fatigued Drivers

2020 ◽  
Vol 17 (22) ◽  
pp. 8499
Author(s):  
Chao Zeng ◽  
Wenjun Wang ◽  
Chaoyang Chen ◽  
Chaofei Zhang ◽  
Bo Cheng
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sex Differences ◽  
Frequency Domain ◽  
Time Domain ◽  
Mental States ◽  
Male And Female ◽  
Frequency Domain Methods ◽  
The Difference ◽  
Electrocardiogram Ecg

The effects of fatigue on a driver’s autonomic nervous system (ANS) were investigated through heart rate variability (HRV) measures considering the difference of sex. Electrocardiogram (ECG) data from 18 drivers were recorded during a simulator-based driving experiment. Thirteen short-term HRV measures were extracted through time-domain and frequency-domain methods. First, differences in HRV measures related to mental state (alert or fatigued) were analyzed in all subjects. Then, sex-specific changes between alert and fatigued states were investigated. Finally, sex differences between alert and fatigued states were compared. For all subjects, ten measures showed significant differences (Mann-Whitney U test, p < 0.01) between different mental states. In male and female drivers, eight and four measures, respectively, showed significant differences between different mental states. Six measures showed significant differences between males and females in an alert state, while ten measures showed significant sex differences in a fatigued state. In conclusion, fatigue impacts drivers’ ANS activity, and this impact differs by sex; more differences exist between male and female drivers’ ANS activity in a fatigued state than in an alert state.

Heart Rate Variability in Diabetes Patients

2006 ◽  
Vol 34 (3) ◽  
pp. 291-296 ◽  
Author(s):  
H Kudat ◽  
V Akkaya ◽  
AB Sozen ◽  
S Salman ◽  
S Demirel ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Frequency Domain ◽  
Autonomic Neuropathy ◽  
Time Domain ◽  
Cardiovascular Autonomic Neuropathy ◽  
Chronic Complications ◽  
Frequency Power ◽  
Diabetes Patients

Diabetes mellitus can cause cardiovascular autonomic neuropathy and is associated with increased cardiovascular deaths. We investigated cardiovascular autonomic neuropathy in diabetics and healthy controls by analysis of heart rate variability. Thirty-one diabetics and 30 age- and sex-matched controls were included. In the time domain we measured the mean R-R interval (NN), the standard deviation of the R-R interval index (SDNN), the standard deviation of the 5-min R - R interval mean (SDANN), the root mean square of successive R - R interval differences (RMSSD) and the percentage of beats with a consecutive R - R interval difference > 50 ms (pNN50). In the frequency domain we measured high-frequency power (HF), low-frequency power (LF) and the LF/HF ratio. Diabetes patients had lower values for time-domain and frequency-domain parameters than controls. Most heart rate variability parameters were lower in diabetes patients with chronic complications than in those without chronic complications.

EFFECT OF PHYSICAL ACTIVITIES ON HEART RATE VARIABILITY AND SKIN CONDUCTANCE

2021 ◽  
pp. 2150038
Author(s):  
Ankita Soni ◽  
Kirti Rawal
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Skin Conductance ◽  
Time Domain ◽  
Low Frequency ◽  
Physical Activities ◽  
Rr Intervals ◽  
Warm Up ◽  
Frequency Domain Methods

The sympathetic and parasympathetic function of the Autonomic Nervous System[Formula: see text]ANS[Formula: see text] is the primary cause of the variations in Heart Rate and Skin Conductance[Formula: see text]SC[Formula: see text] during different physical activities. This paper aims to analyze the effect of different physical activities i.e. (a) Supine (b) Standing and (c) Warm-up, on Heart Rate Variability (HRV) and SC. The standard dataset of 18 subjects has been used to analyze the effect of physical activities on the HRV and SC. In the used dataset, the subjects are in supine, standing, and warm-up positions. The linear methods (time domain & frequency domain) of HRV are implemented on the standard dataset for analyzing the effect of physical activities. It has been observed with the analysis of the HRV that the mean value of time domain methods i.e. the NN interval’s standard deviation (SDNN), the successive RR interval’s root mean square (RMSSD), RR intervals with more than 50 ms differences in between them (NN50), percentage of successive RR intervals that have the difference of more than 50 ms (pNN50) are decreased and the value of Heart rate (HR) increased when the activity has been changed from supine to standing and standing to the warm-up positions. The value of frequency domain methods, such as low frequency (LF) and the ratio of low and high frequency (LF/HF) increased, while the value of HF decreases as activity changes from supine to standing and from supine to warm-up position. Further, the increment is also observed in the value of SC when activity is switched from supine to standing and from standing to the warm-up position. It is concluded from the results that there is a significant decrement that is observed in the value of HRV, while the increment is observed in the value of SC and HR. Decrement of HRV reflects that the sympathetic activity is increased as activity changed from supine to standing and further from standing to warm-up positions.

Investigation of heart rate variability of patients undergoing coronary artery bypass grafting using the statistical signal characterization method

2021 ◽  
pp. 1-12
Author(s):  
Abdulnasir Hossen
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Frequency Domain ◽  
Time Domain ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
The Status ◽  
Operation Group ◽  
Signal Characterization

BACKGROUND: Autonomic function can be estimated non-invasively using heart rate variability (HRV). HRV of patients undergoing coronary artery bypass grafting (CABG) is investigated in time-domain and frequency-domain before and after CABG to study the effect of operation on the status of patients. OBJECTIVE: The main purpose of this work is to evaluate the effect of CABG surgery on patients with ischemic heart disease (IHD) before operation, and to monitor the status of patients on day 6 and day 30 after the CABG operation. METHODS: The statistical signal characterization (SSC) technique is used in this work in order to derive different morphology-based parameters to indirectly describe time-domain and frequency-domain HRV parameters in 24 patients undergoing CABG operation, before the operation (Group 1: G1), 6 days after operation (Group 2: G2) and 30 days after operation (Group 3: G3). The data is obtained from the Sultan Qaboos University Hospital in Oman. RESULTS: The SSC parameters Mean(mt) and Mean(dt) are reduced in all 24 patients and in 23 out of 24 patients in G2 compared to G1 (6-days after operation compared with before operation), respectively. Comparing G3 to G1 the reduction in Mean(mt) and Mean(dt) is noted in 18 of the 24 patients. CONCLUSIONS: The parameters Mean(mt) and Mean(dt) are successful parameters to follow the HRV for patients undergoing CABG surgery. A relation between those SSC parameters and the HRV time-domain and frequency-domain parameters is investigated in this paper to understand the physiological behavior of the patients.

scholarly journals Time domain and frequency domain analysis of heart rate variability in elite Nepalese football players

2015 ◽  
Vol 6 (9) ◽  
pp. 641 ◽  
Author(s):  
Deepak Sharma ◽  
Bishnu Hari Paudel ◽  
Rita Khadka ◽  
Dilip Thakur ◽  
Niraj Khatri Sapkota ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Time Domain ◽  
Domain Analysis ◽  
Frequency Domain Analysis ◽  
Football Players

Associations between heart rate variability, metabolic syndrome risk factors, and insulin resistance

2015 ◽  
Vol 40 (7) ◽  
pp. 734-740 ◽  
Author(s):  
Melanie I. Stuckey ◽  
Antti Kiviniemi ◽  
Dawn P. Gill ◽  
J. Kevin Shoemaker ◽  
Robert J. Petrella
Keyword(s):  
Risk Factors ◽  
Insulin Resistance ◽  
Metabolic Syndrome ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Standard Deviation ◽  
Waist Circumference ◽  
Frequency Domain ◽  
Model Assessment ◽  
Poincaré Plot

The purpose of this study was to examine differences in heart rate variability (HRV) in metabolic syndrome (MetS) and to determine associations between HRV parameters, MetS risk factors, and insulin resistance (homeostasis model assessment for insulin resistance (HOMA-IR)). Participants (n = 220; aged 23–70 years) were assessed for MetS risk factors (waist circumference, blood pressure, fasting plasma glucose, triglycerides, and high-density lipoprotein cholesterol) and 5-min supine HRV (time and frequency domain and nonlinear). HRV was compared between those with 3 or more (MetS+) and those with 2 or fewer MetS risk factors (MetS–). Multiple linear regression models were built for each HRV parameter to investigate associations with MetS risk factors and HOMA-IR. Data with normal distribution are presented as means ± SD and those without as median [interquartile range]. In women, standard deviation of R–R intervals 38.0 [27.0] ms, 44.5 [29.3] ms; p = 0.020), low-frequency power (5.73 ± 1.06 ln ms2, 6.13 ± 1.05 ln ms2; p = 0.022), and the standard deviation of the length of the Poincaré plot (46.8 [31.6] ms, 58.4 [29.9] ms; p = 0.014) were lower and heart rate was higher (68 [13] beats/min, 64 [12] beats/min; p = 0. 018) in MetS+ compared with MetS–, with no differences in men. Waist circumference was most commonly associated with HRV, especially frequency domain parameters. HOMA-IR was associated with heart rate. In conclusion, MetS+ women had a less favourable HRV profile than MetS– women, but there were no differences in men. HOMA-IR was associated with heart rate, not HRV.

scholarly journals Analysis of Heart Rate Variability to Predict Patient Age in a Healthy Population

2007 ◽  
Vol 46 (02) ◽  
pp. 191-195 ◽  
Author(s):  
M. Matteucci ◽  
L. Mainardi ◽  
V. D. Corino
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Healthy Subjects ◽  
Principal Component ◽  
Healthy Population ◽  
Good Prediction ◽  
Patient Age ◽  
Age Related ◽  
Patient Âgé ◽  
Non Linear

Summary Objectives : To estimate age of healthy subjects by means of the heart rate variability (HRV) parameters thus assessing the potentiality of HRV indexes as a biomarker of age. Methods : Long-term indexes of HRV in time domain, frequency domain and non-linear parameters were computed on 24-hour recordings in a dataset of 63 healthy subjects (age range 20-76 years old). Then, as interbeat dynamics markedly change with age, showing a reduced HRV in older subjects, we tried to capture age-related influence on HRV by principal component analysis and to predict the subject age by means of a feedforward neural network. Results : The network provides good prediction of patient age, even if a slight overestimation in the younger subjects and a slight underestimation in the older ones were observed. In addition, the important contribution of non-linear indexes to prediction is underlined. Conclusions : HRV as a predictor of age may lead to the definition of a new biomarker of aging.

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