scholarly journals Variations of Time Irreversibility of Heart Rate Variability Under Normobaric Hypoxic Exposure

2021 ◽  
Vol 12 ◽  
Author(s):  
Yang Li ◽  
Jianqing Li ◽  
Jian Liu ◽  
Yong Xue ◽  
Zhengtao Cao ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Delay Time ◽  
Normal Sinus Rhythm ◽  
Hypoxic Exposure ◽  
Control Group ◽  
Parasympathetic Nerves ◽  
Normal Sinus ◽  
Time Irreversibility ◽  
The Difference

In the field of biomedicine, time irreversibility is used to describe how imbalanced and asymmetric biological signals are. As an important feature of signals, the direction of time is always ignored. To find out the variation regularity of time irreversibility of heart rate variability (HRV) in the initial stage of hypoxic exposure, the present study implemented 2 h acute normobaric hypoxic exposure on six young subjects who have no plateau or hypoxia experiences; oxygen concentration was set as 12.9%. Electrocardiogram (ECG) signals were recorded in the whole process and RR interval sequences were extracted. Mathematical operations were executed to transform the difference of adjacent RR intervals into proportion and distance with delay time to conduct time irreversibility analysis of HRV. The same calculating method was implemented on six items randomly picked out from the MIT-BIH normal sinus rhythm database as a control group. Results show that variation of time irreversibility of HRV in a hypoxic environment is different from that in a normoxic environment, time irreversibility indices of a hypoxic group decreases continually at a delay time of 1 and 2, and indices curves of time irreversibility gradually tend to be steady and gather with each other at a delay time of 3 or 4. The control group shows no consistent tendency no matter what the delay time is in the range of 1–4. Our study indicates that in short-time hypoxic exposure, as hypoxic time goes by, regulation of the cardiovascular autonomic nervous system weakens; regulation times and intensity of sympathetic and parasympathetic nerves tend to be equal.

scholarly journals Detection of coronary artery disease by reduced features and extreme learning machine

2018 ◽  
Vol 91 (2) ◽  
pp. 166-175 ◽  
Author(s):  
Ram Sewak Singh ◽  
Barjinder Singh Saini ◽  
Ramesh Kumar Sunkaria
Keyword(s):  
Coronary Artery Disease ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Coronary Artery ◽  
Discriminant Analysis ◽  
Normal Sinus Rhythm ◽  
Normal Sinus ◽  
Hidden Node ◽  
Artery Disease ◽  
Learning Machine

Objective. Cardiovascular diseases generate the highest mortality in the globe population, mainly due to coronary artery disease (CAD) like arrhythmia, myocardial infarction and heart failure. Therefore, an early identification of CAD and diagnosis is essential. For this, we have proposed a new approach to detect the CAD patients using heart rate variability (HRV) signals. This approach is based on subspaces decomposition of HRV signals using multiscale wavelet packet (MSWP) transform and entropy features extracted from decomposed HRV signals. The detection performance was analyzed using Fisher ranking method, generalized discriminant analysis (GDA) and binary classifier as extreme learning machine (ELM). The ranking strategies designate rank to the available features extracted by entropy methods from decomposed heart rate variability (HRV) signals and organize them according to their clinical importance. The GDA diminishes the dimension of ranked features. In addition, it can enhance the classification accuracy by picking the best discerning of ranked features. The main advantage of ELM is that the hidden layer does not require tuning and it also has a fast rate of detection.Methodology. For the detection of CAD patients, the HRV data of healthy normal sinus rhythm (NSR) and CAD patients were obtained from  a standard database. Self recorded data as normal sinus rhythm (Self_NSR) of healthy subjects were also used in this work. Initially, the HRV time-series was decomposed to 4 levels using MSWP transform. Sixty two features were extracted from decomposed HRV signals by non-linear methods for HRV analysis, fuzzy entropy (FZE) and Kraskov nearest neighbour entropy (K-NNE). Out of sixty-two features, 31 entropy features were extracted by FZE and 31 entropy features were extracted by K-NNE method. These features were selected since every feature has a different physical premise and in this manner concentrates and uses HRV signals information in an assorted technique. Out of 62 features, top ten features were selected, ranked by a ranking method called as Fisher score. The top ten features were applied to the proposed model, GDA with Gaussian or RBF kernal + ELM having hidden node as sigmoid or multiquadric. The GDA method transforms top ten features to only one feature and ELM has been used for classification.Results. Numerical experimentations were performed on the combination of datasets as NSR-CAD and Self_NSR- CAD subjects. The proposed approach has shown better performance using top ten ranked entropy features. The GDA with RBF kernel + ELM having hidden node as multiquadric method and GDA with Gaussian kernel + ELM having hidden node as sigmoid or multiquadric method achieved an approximate detection accuracy of 100% compared to ELM and linear discriminant analysis (LDA)+ELM for both datasets. The subspaces level-4 and level-3 decomposition of HRV signals by MSWP transform can be used for detection and analysis of CAD patients.

Nonlinear Characterization of Heart Rate Variability in Normal Sinus Rhythm, Atrial Fibrillation and Congestive Heart Failure

2016 ◽  
Author(s):  
Syed Hassan Zaidi ◽  
Imran Akhtar ◽  
Syed Imran Majeed ◽  
Tahir Zaidi ◽  
Muhammad Saif Ullah Khalid
Keyword(s):  
Heart Failure ◽  
Atrial Fibrillation ◽  
Heart Rate ◽  
Congestive Heart Failure ◽  
Heart Rate Variability ◽  
Sinus Rhythm ◽  
Time Series Data ◽  
Normal Sinus Rhythm ◽  
Series Data ◽  
Normal Sinus

This paper highlights the application of methods and techniques from nonlinear analysis to illustrate their far superior capability in revealing complex cardiac dynamics under various physiological and pathological states. The purpose is to augment conventional (time and frequency based) heart rate variability analysis, and to extract significant prognostic and clinically relevant information for risk stratification and improved diagnosis. In this work, several nonlinear indices are estimated for RR intervals based time series data acquired for Healthy Sinus Rhythm (HSR) and Congestive Heart Failure (CHF), as the two groups represent different cases of Normal Sinus Rhythm (NSR). In addition to this, nonlinear algorithms are also applied to investigate the internal dynamics of Atrial Fibrillation (AFib). Application of nonlinear tools in normal and diseased cardiovascular states manifest their strong ability to support clinical decision support systems and highlights the internal complex properties of physiological time series data such as complexity, irregularity, determinism and recurrence trends in cardiovascular regulation mechanisms.

scholarly journals Impact of heart rate variability in patients with normal sinus rhythm on image quality in coronary magnetic angiography

2008 ◽  
Vol 28 (1) ◽  
pp. 74-79 ◽  
Author(s):  
Tarinee Tangcharoen ◽  
Cosima Jahnke ◽  
Uwe Koehler ◽  
Bernhard Schnackenburg ◽  
Christoph Klein ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Image Quality ◽  
Sinus Rhythm ◽  
Normal Sinus Rhythm ◽  
Normal Sinus

Lower Heart Rate Variability Associated With Military Sexual Trauma Rape and Posttraumatic Stress Disorder

2012 ◽  
Vol 14 (4) ◽  
pp. 412-418 ◽  
Author(s):  
Elizabeth Ann Davis Lee ◽  
Sue A. Theus
Keyword(s):  
Heart Rate ◽  
Posttraumatic Stress Disorder ◽  
Heart Rate Variability ◽  
Stress Disorder ◽  
Normal Sinus Rhythm ◽  
Military Sexual Trauma ◽  
Female Veterans ◽  
Normal Sinus ◽  
Cardiac Symptoms ◽  
The Mean

Low heart rate variability (HRV) can occur with psychological disorders such as posttraumatic stress disorder (PTSD). The purpose of this study was to examine the association between PTSD by trauma type and decreased HRV measures in female veterans with cardiac symptoms. This secondary analysis utilized data from a previous study of female veterans ( n = 125) examined for cardiac symptoms by Holter and electrocardiogram recordings at a Veterans Affairs medical center. The mean HRV measure from three 10-s data segments with spontaneous respirations was obtained for each subject. PTSD diagnosis and type of trauma exposure were collected from mental health consult notes. Chi-square was used for frequency of subject characteristics; independent t tests and one-way analysis of variance (ANOVA) compared means of HRV measures between trauma types. Statistical significance was set at p < .05 a priori. By ANOVA, significantly lower log-transformed standard deviation of all normal sinus rhythm R-R intervals (SDNN) and log-transformed square root of the mean of the sum of the squares of differences between adjacent normal sinus rhythm R-R intervals (RMSSD) were found in the PTSD group with documented rape military sexual trauma (MST) compared to other groups including no PTSD, PTSD following MST with rape not specified, combat exposure, and nonmilitary-related trauma; lower HRV measures were not found with other PTSD types of trauma. This study suggests rape MST with concomitant PTSD may be a risk factor for decreased HRV in female veterans examined for cardiac symptoms.

The effect of sinus node depression on heart rate variability in humans using zatebradine, a selective bradycardic agent

1998 ◽  
Vol 76 (7-8) ◽  
pp. 806-810 ◽  
Author(s):  
Yaariv Khaykin ◽  
Paul Dorian ◽  
Anthony Tang ◽  
M Green ◽  
Jan Mitchell ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Sinus Node ◽  
Isotonic Saline ◽  
Low Frequency ◽  
Structural Heart Disease ◽  
Total Power ◽  
Control Group ◽  
Bradycardic Agent ◽  
Normal Sinus

Zatebradine is a bradycardic agent with a selective effect on the pacemaker current in the sinus node. The effect of such drugs on heart rate variability is not known. Thirty-six patients without structural heart disease were randomly assigned to receive 10 mg of zatebradine i.v. (n = 24) or isotonic saline (n = 12). Heart rate variability (HRV) was recorded as power in the very low frequency (VLF, 0.003-0.040 Hz), low frequency (LF, 0.040-0.150 Hz), and high frequency (HF, 0.150-0.400 Hz) spectral bands as well as total power (TP, 0.003-0.400 Hz) during 5-min ECG acquisitions at baseline, 30, and 60 min following the start of the infusion. No change in heart rate variability was detected in the control group. Zatebradine significantly reduced heart rate variability at 60 min in all frequency bands: VLF (-12 ± 4%, p < 0.001), LF (-19 ± 4%, p < 0.001), and HF (-26 ± 5%, p < 0.001). The reduction in HRV following zatebradine is due to depression of sinus node response to all external stimuli and underscores the need for documentation of normal sinus node function in HRV research.Key words: zatebradine, sinus node, heart rate variability, HRV, autonomic nervous system.

scholarly journals Comparative Analysis of Heart Rate Variability Parameters for Arrhythmia and Atrial Fibrillation using ANOVA

2018 ◽  
Vol 11 (4) ◽  
pp. 1841-1849 ◽  
Author(s):  
Kirti Kirti ◽  
Harsh Sohal ◽  
Shruti Jain
Keyword(s):  
Atrial Fibrillation ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Normal Sinus Rhythm ◽  
Health Conditions ◽  
Statistical Comparison ◽  
Cardiac Health ◽  
Normal Sinus ◽  
Gender And Health ◽  
Anova Technique

Heart Rate Variability (HRV) is an important criterion to check the cardiac health. Sudden HRV signifies the unhealthy condition of the heart, particularly when the person is suffering from a cardiac disease. HRV parameters on different patients of different ages, gender and health conditions are observed using time domain, geometrical domain and frequency domain. Statistical comparison is done on three different databases MIT/BIH Normal Sinus Rhythm (NSR), MIT/BIH Arrhythmia (AR) and MIT/BIH Atrial Fibrillation (AF) using Analysis of Variance (ANOVA) technique. We have extracted twenty HRV features from all the three domains, which show weak, moderate or strong significant changes as per the relation during comparison with respective databases. Out of twenty only nine features are selected which shows noticeable difference between three databases. Later, the selected features will be used for classification in future.

scholarly journals Peculiarities of the autonomic balance assessed through heart rate variability analysis in sportsmen and nonsportsmen

2008 ◽  
pp. 17-25 ◽  
Author(s):  
Oto Barak ◽  
Oleg Glazacev ◽  
Helena Dudnik ◽  
Irina Korobeinikova ◽  
Aleksandar Klasnja ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Frequency Domain ◽  
Control Group ◽  
Heart Rate Variability Analysis ◽  
Autonomic Balance ◽  
Basketball Players ◽  
Significant Difference ◽  
The Difference ◽  
Novi Sad

A comparative study was used to analyze the difference in autonomic balance assessed by time and frequency domain parameters of heart rate variability (HRV) between students athletes and non-sportsmen. Five-minute digital ECG trays were recorded in 21 students - athletes, 10 basketball players recruited from first league clubs of No- vi Sad and the Serbian representatives and 11 rowers from the Novi Sad rowing club 'Danubius'. The control group was formed by 15 non-sportsmen, students of the Medical faculty in Novi Sad who underwent the same registrations. Time and frequency-domain of HRV were analyzed by a software developed by the company 'Neurosoft', VNS-Spektr, Ivanovo, Russia. Resting heart rate in athletes was significantly lower (p < 0.01) than in non-sportsmen. In time-domain parameters HRV significantly higher values were present in the group of sportsmen as opposed to non-sportsmen RRNN (p < 0.01), RMSSD (p < 0.02) and pNN50 (p < 0.01). In frequency-domain of HRV statistically significant difference between the two groups was observed only in normalized values of LF and HF (p < 0.05) and their ratio LF/HF (p < 0.02). LFn was larger in non-sportsmen than in students-athletes. On the other hand HFn was larger in athletes than in non-sportsmen. The LF/HF ratio was larger in non-sportsmen (2.87 0.34) than in athletes (1.91 0.20). After dividing the athletes recruited for this investigation into two groups (basketball players and rowers) significant level of difference (p < 0.05) in HRV data was present only in the VLF spectrum (2060.55 290.68 ms2 for rowers and 1303.30 ? 169.95 ms2 for basketball players).

Influence of forced respiration on nonlinear dynamics in heart rate variability

1997 ◽  
Vol 272 (4) ◽  
pp. R1149-R1154 ◽  
Author(s):  
J. K. Kanters ◽  
M. V. Hojgaard ◽  
E. Agner ◽  
N. H. Holstein-Rathlou
Keyword(s):  
Nonlinear Dynamics ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Prediction Error ◽  
Correlation Dimension ◽  
Normal Sinus Rhythm ◽  
Spontaneous Respiration ◽  
Nonlinear Prediction ◽  
Normal Sinus ◽  
Nonlinear Input

Although it is doubtful whether the normal sinus rhythm can be described as low-dimensional chaos, there is evidence for inherent nonlinear dynamics and determinism in time series of consecutive R-R intervals. However, the physiological origin for these nonlinearities is unknown. The aim of this study was to test whether the known nonlinear input from spontaneous respiration is a source for the nonlinearities in heart rate variability. Twelve healthy subjects were examined in supine position with 3-h electrocardiogram recordings during both spontaneous and forced respiration in accordance with a metronome set to 12 min(-1). Nonlinear dynamics were measured as the correlation dimension and the nonlinear prediction error. Complexity expressed as correlation dimension was unchanged from normal respiration, 9.1 +/- 0.5, compared with forced respiration, 9.3 +/- 0.6. Also, nonlinear determinism expressed as the nonlinear prediction error did not differ between spontaneous respiration, 32.3 +/- 3.4 ms, and forced respiration, 31.9 +/- 5.7. It is concluded that the origin of the nonlinear dynamics in heart rate variability is not a nonlinear input from the respiration into the cardiovascular oscillator. Additional studies are needed to elucidate the mechanisms behind the nonlinear dynamics in heart rate variability.

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