scholarly journals Autonomic Nervous System Assessment- Wide Role of Heart Rate Variability Analysis- A Review

2011 ◽  
Vol 4 (1) ◽  
pp. 403-405
Author(s):  
Gul Ar Navi Khan Gul Ar Navi Khan ◽  
◽  
Nazia Ishrat
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Autonomic Nervous ◽  
System Assessment

Heart rate variability analysis of students with different motor activity levels

2021 ◽  
Vol 25 (2) ◽  
pp. 127-135
Author(s):  
A. S. Emelyanova ◽  
L. A. Simonyan ◽  
E. E. Stepura
Keyword(s):  
Physical Activity ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Motor Activity ◽  
Heart Rate Variability Analysis ◽  
Variability Analysis ◽  
Autonomic Nervous ◽  
Vegetative Status

Relevance. Assessment of the functional state of the body is one of the leading tasks of physiology. The article deals with the analysis of the initial vegetative status of students with different levels of motor activity. Materials and Methods. Registration and analysis of the heart rate variability was carried out with the help of a modern complex electrophysiological laboratory CONAN - 4.5. The heart activity of students engaged in physical culture within the educational process was evaluated on the basis of heart rate variability analysis. Results and Discussion. It was revealed that among the entire studied array of students (with the differentiation of the initial vegetative status calculated according to muscle tension index), normotonics are characterized by an optimal ratio between the parasympathetic and sympathetic divisions of the autonomic nervous system. At the same time, the value of the coefficient of physical activity in the studied group was determined at the level of 1.730.1. Conclusion. For vagotonics, the value of the triangular index was 2.50.2 conventional units (CU), which confirms the idea of an increase in the influence on the autonomic nervous system. The value for normotonics is 2.20.1 CU. This group was characterized by the balance between the sympathetic and parasympathetic parts of the autonomic nervous system. In sympathicotonics - 1.90.5 CU, which confirms the idea of increasing the influence of the sympathetic division of the autonomic nervous system. In hypersympathicotonics-1.10.4 CU. To ensure adequate functioning of the cardiovascular system and for normal adaptation to physical exertion in students, it is necessary to form a level of motor activity that quantitatively corresponds to a coefficient of physical activity of at least 1.75.

19. Role of the autonomic nervous system in blood pressure and heart rate variability in conscious rats

1991 ◽  
Vol 9 (6) ◽  
pp. S429
Author(s):  
C. Cerutti ◽  
M. Lo ◽  
Claude Julien ◽  
Madelaine Vincent ◽  
C. Paultre ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Conscious Rats ◽  
Autonomic Nervous

scholarly journals Maturation of the Autonomic Nervous System in Premature Infants: Estimating Development Based on Heart-Rate Variability Analysis

2021 ◽  
Vol 11 ◽  
Author(s):  
Mario Lavanga ◽  
Elisabeth Heremans ◽  
Jonathan Moeyersons ◽  
Bieke Bollen ◽  
Katrien Jansen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Premature Infants ◽  
Low Frequency ◽  
Absolute Error ◽  
Heart Rate Variability Analysis ◽  
Autonomic Nervous ◽  
Comparable Performance

This study aims at investigating the development of premature infants' autonomic nervous system (ANS) based on a quantitative analysis of the heart-rate variability (HRV) with a variety of novel features. Additionally, the role of heart-rate drops, known as bradycardias, has been studied in relation to both clinical and novel sympathovagal indices. ECG data were measured for at least 3 h in 25 preterm infants (gestational age ≤32 weeks) for a total number of 74 recordings. The post-menstrual age (PMA) of each patient was estimated from the RR interval time-series by means of multivariate linear-mixed effects regression. The tachograms were segmented based on bradycardias in periods after, between and during bradycardias. For each of those epochs, a set of temporal, spectral and fractal indices were included in the regression model. The best performing model has R2 = 0.75 and mean absolute error MAE = 1.56 weeks. Three main novelties can be reported. First, the obtained maturation models based on HRV have comparable performance to other development models. Second, the selected features for age estimation show a predominance of power and fractal features in the very-low- and low-frequency bands in explaining the infants' sympathovagal development from 27 PMA weeks until 40 PMA weeks. Third, bradycardias might disrupt the relationship between common temporal indices of the tachogram and the age of the infant and the interpretation of sympathovagal indices. This approach might provide a novel overview of post-natal autonomic maturation and an alternative development index to other electrophysiological data analysis.

scholarly journals Multiscale Fine-Grained Heart Rate Variability Analysis for Recognizing the Severity of Hypertension

2019 ◽  
Vol 2019 ◽  
pp. 1-9 ◽  
Author(s):  
Hongbo Ni ◽  
Ying Wang ◽  
Guoxing Xu ◽  
Ziqiang Shao ◽  
Wei Zhang ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Good Method ◽  
Heart Rate Variability Analysis ◽  
Time Frequency ◽  
Autonomic Nervous ◽  
Fine Grained ◽  
Multifactor Analysis

Hypertension is a common and chronic disease and causes severe damage to patients’ health. Blood pressure of a human being is controlled by the autonomic nervous system. Heart rate variability (HRV) is an impact of the autonomic nervous system and an indicator of the balance of the cardiac sympathetic nerve and vagus nerve. HRV is a good method to recognize the severity of hypertension due to the specificity for prediction. In this paper, we proposed a novel fine-grained HRV analysis method to enhance the precision of recognition. In order to analyze the HRV of the patient, we segment the overnight electrocardiogram (ECG) into various scales. 18 HRV multidimensional features in the time, frequency, and nonlinear domain are extracted, and then the temporal pyramid pooling method is designed to reduce feature dimensions. Multifactor analysis of variance (MANOVA) is applied to filter the related features and establish the hypertension recognizing model with relevant features to efficiently recognize the patients’ severity. In this paper, 139 hypertension patients’ real clinical ECG data are applied, and the overall precision is 95.1%. The experimental results validate the effectiveness and reliability of the proposed recognition method in the work.

scholarly journals Heart Rate Variability Analysis in Risk of Asthma Stratification

2017 ◽  
Vol 5 ◽  
Author(s):  
Javier Milagro ◽  
Eduardo Gil ◽  
Jesús Lázaro ◽  
Ville-Pekka Seppä ◽  
L. Pekka Malmberg ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Early Diagnosis ◽  
Heart Rate Variability Analysis ◽  
Long Term Effects ◽  
Variability Analysis ◽  
Hrv Analysis

Early diagnosis of asthma is crucial to avoid long-term effects such as permanent airway obstruction. Pathogenesis of asthma has been related with autonomic nervous system (ANS) dysfunction, concretely with abnormal parasympathetic activity. As heart rate variability (HRV) analysis does reflect ANS activity, it has been employed here in risk of asthma stratification.

CONFIRMATION OF THE CLINICAL VALUE AND THE EFFECT OF SOUND BIOFEEDBACK ON THE AUTONOMIC NERVOUS SYSTEM THROUGH HEART RATE VARIABILITY ANALYSIS

2021 ◽  
pp. 2150036
Author(s):  
DAECHANG KIM ◽  
SEUNGBONG LEE ◽  
SUNGMIN KIM ◽  
JAEHOON JEONG
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Previous History ◽  
Low Frequency ◽  
Sympathetic Nerves ◽  
Heart Rate Variability Analysis ◽  
Clinical Value ◽  
Autonomic Nervous

The purpose of this study is to suggest sound biofeedback, which is a new technique of early stress relief effect by observing change in the heart rate variability (HRV). The sound biofeedback imitating heart rate of the comfortable and stress state is termed parasympathetic stimulation sound (PSS) and sympathetic stimulation sound (SSS), respectively. Twelve subjects were selected without previous history of cardiovascular diseases and mental illness, such as arrhythmia, myocardial infarction, depression and panic disorder. To confirm the changes in the low-frequency (LF), high-frequency (HF) and LF/HF values of HRV as stress evaluation indicators, the HRV of subjects was measured by photoplethysmogram. Signals were processed using the peak detect algorithm, and fast Fourier transform. Results were obtained using power specific densities. During the PSS stimulation, the LF/HF tended to decrease generally. On the other hand, during the SSS stimulation, LF/HF tended to increase. The LF/HF Mean change value ([Formula: see text]) using the PSS stimulation is similar to the effect of Transcutaneous Vagal Nerve Stimulation (tVNS). In addition, the quantitative effect of sound biofeedback was confirmed by judging changes in the parasympathetic and sympathetic nerves in the autonomic nervous system (ANS) through [Formula: see text]-score normalized data. These experimental results suggest that sound biofeedback has the same stimulation location and clinical applicability as tVNS. As a result, sound biofeedback may be used as a new method for stress reduction.

Analysis of Heart rate Variability and Implication of Different Factors on Heart Rate Variability

2020 ◽  
Vol 16 ◽  
Author(s):  
Reena Tiwari ◽  
Ravindra Kumar ◽  
Sujata Malik ◽  
Tilak Raj ◽  
Punit Kumar
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nervous System ◽  
Autonomic Nervous System ◽  
Parasympathetic Nervous System ◽  
Invasive Technique ◽  
Clinical Practices ◽  
Autonomic Nervous ◽  
Pathological Conditions

Background:: The heart is the central organ of the circulatory system which maintains the flow of blood along with the transport of nutrients to different cells and tissues. A well-functioning cardiac state is a complicated mode of changeability. A healthy heart is not only about oscillation as the rhythmometer is not the same in every circumstance. Heart rate shows variations so that it can be regulated according to psychophysiological conditions to maintain the effect of the internal-external stimulus. Objective:: The main objective of this review is to provide a piece of all-inclusive information about heart rate variability (HRV) and different variables affecting HRV. The direct interconnection among factors and so that HRV can be used in clinical practices. Methods:: This review article contains a detailed survey of literature about HRV available in different online sources such as; Google Scholar, Science Direct, PubMed, and Web of Science, etc. In this review, the authors have focused on the role of the autonomic nervous system in the regulation of HRV and the role of various factors affecting HRV. Results:: The variation in the time between two heartbeats is termed as HRV. It is one of the indicators of many pathological conditions related to cardiovascular health. It provided reliable information about the interaction of the sympathetic and parasympathetic nervous systems. The analysis of the variation of heart rate is a well-known non-invasive technique to identify the functioning of the autonomic nervous system. The autonomic nervous system (ANS) depends on the sympathetic and parasympathetic nervous system for transferring information. The cardio-accelerating center, lungs, and non-striated muscles are innervated by cardiac sympathetic nerves. This division of ANS latches upon the heart accordingly via the cervicothoracic ganglion and vagus nerve. It is found that cardiac normal variability depends upon this stimulation towards the sinoatrial node (pacemaker) which can be evaluated by analyzing the HRV. In human- based studies, it has been found that low level of HRV is one of the main causes of death rate among adults. Hence, HRV helps in identifying the risk of cardiac diseases and the state of ANS. Conclusion:: The heart plays a vital role in the human body and the well-functioning of the cardiac system is the need for a healthy life. The heart contains its nervous system termed as neurocardio system in which ANS plays a key role in which the sympathetic and parasympathetic system interplay to regulate HRV. High HRV is associated with healthy condition while low HRV is associated with pathological conditions. The HRV is influenced by various variables such as; pathological, physiological, psychological, environmental factors, lifestyle factors, and genetic factors, etc.

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