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.

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

Background: Heart rate variability (HRV) as an accurate, noninvasive measure of the Autonomous Nervous System (ANS) can reflect mental health (e.g., stress, depression, or anxiety). Tai Chi and Yoga (Tai Chi/Yoga), as the most widely practiced mind–body exercises, have shown positive outcomes of mental health. To date, no systematic review regarding the long-lasting effects of Tai Chi/Yoga on HRV parameters and perceived stress has been conducted. Objective: To critically evaluate the existing literature on this topic. Methods: Five electronic databases (Web of Science, PubMed, Scopus, SportDiscus and Cochrane Library) were searched from the start of the research project to July 2018. Study selection, data extraction, and study quality assessment were independently carried out by two reviewers. The potentially identified randomized controlled trials (RCT) reported the useful quantitative data that were included only for meta-analysis. Results: meta-analysis of 17 medium-to-high quality RCTs showed significantly beneficial effects on HRV parameters (normalized low-frequency, Hedge’s g = −0.39, 95% CI −0.39 to −0.56, p < 0.001, I2 = 11.62%; normalized high-frequency, Hedge’s g = 0.37, 95% CI 0.22 to −0.52, p < 0.001, I2 = 0%; low-frequency to high-frequency ratio, Hedge’s g = −0.58, 95% CI −0.81 to −0.35, p < 0.001, I2 = 53.78%) and stress level (Hedge’s g = −0.80, 95% CI −1.17 to −0.44, p < 0.001, I2 = 68.54%). Conclusions: Stress reduction may be attributed to sympathetic-vagal balance modulated by mind–body exercises. Tai Chi/Yoga could be an alternative method for stress reduction for people who live under high stress or negative emotions.

https://www.mdpi.com/2077-0383/7/11/404/pdf

Effects of Mind⁻Body Exercises (Tai Chi/Yoga) on Heart Rate Variability Parameters and Perceived Stress: A Systematic Review with Meta-Analysis of Randomized Controlled Trials

The autonomic nervous system as operating system of the human organism permeats all organ systems with its pathways permeating that it is involved with virtually all diseases. Anatomically a central part, an afferent part and sympathetic and parasympathetic efferent system can be distinguished. Among the different functional subsystems of the autonomic nervous system, the cardiovascular autonomic nervous system is most frequently examined with easily recordable cardiovascular biosignals as heart rate and blood pressure. Although less widely established, sudomotor tests pose a useful supplement to cardiovascular autonomic assessment as impaired neurogenic sweating belongs to the earliest clinical signs of various autonomic neuropathies as well as neurodegenerative disorders and significantly reduces quality of life. Clinically at first, the autonomic nervous system is assessed with a detailed history of clinical autonomic function and a general clinical examination. As a lof of confounding factors can influence autonomic testing, subjects should be adequately prepared in a standardized way. Autonomic testing is usually performed in that way that the response of the autonomic nervous system to a well-defined challenge is recorded. As no single cardiovascular autonomic test is sufficiently reliable, it is recommended to use a combination of different approaches, an autonomic test battery including test to measure parasympathetic and sympathetic cardiovascular function (deep breathing test, Valsalva maneuver, tilt, or pressor test). More specialized tests include carotid sinus massage, assessment of baroreceptor reflex function, pharmacological tests or cardiac, and regional hemodynamic measurements. Techniques to measure functional integrity of sudomotor nerves include the quantitative sudomotor axon reflex sweat test, analysis of the sympathetic skin response as well as the thermoregulatory sweat test. In addition to these rather established techniques more recent developments have been introduced to reduce technical demands and interindividual variability such as the quantitative direct and indirect axon reflex testing or sudoscan. However, diagnostic accuracy of these tests remains to be determined. We reviewed the current literature on currently available autonomic cardiovascular and sudomotor tests with a focus on their physiological and technical mechanisms as well as their diagnostic value in the scientific and clinical setting.

/pdf/the-investigation-of-the-cardiovascular-and-sudomotor-2lxnqcnt8z.pdf

The Investigation of the Cardiovascular and Sudomotor Autonomic Nervous System—A Review

Automated identification of normal and diabetic subjects using HRV signals.Features are extracted using DWT method.Ranked features are subjected to various classifiers.Proposed method classifies diabetes and normal subjects with 92.02% accuracy. Diabetes Mellitus (DM), a chronic lifelong condition, is characterized by increased blood sugar levels. As there is no cure for DM, the major focus lies on controlling the disease. Therefore, DM diagnosis and treatment is of great importance. The most common complications of DM include retinopathy, neuropathy, nephropathy and cardiomyopathy. Diabetes causes cardiovascular autonomic neuropathy that affects the Heart Rate Variability (HRV). Hence, in the absence of other causes, the HRV analysis can be used to diagnose diabetes. The present work aims at developing an automated system for classification of normal and diabetes classes by using the heart rate (HR) information extracted from the Electrocardiogram (ECG) signals. The spectral analysis of HRV recognizes patients with autonomic diabetic neuropathy, and gives an earlier diagnosis of impairment of the Autonomic Nervous System (ANS). Significant correlations with the impaired ANS are observed of the HRV spectral indices obtained by using the Discrete Wavelet Transform (DWT) method. Herein, in order to diagnose and detect DM automatically, we have performed DWT decomposition up to 5 levels, and extracted the energy, sample entropy, approximation entropy, kurtosis and skewness features at various detailed coefficient levels of the DWT. We have extracted relative wavelet energy and entropy features up to the 5th level of DWT coefficients extracted from HR signals. These features are ranked by using various ranking methods, namely, Bhattacharyya space algorithm, t-test, Wilcoxon test, Receiver Operating Curve (ROC) and entropy.The ranked features are then fed into different classifiers, that include Decision Tree (DT), K-Nearest Neighbor (KNN), Naive Bayes (NBC) and Support Vector Machine (SVM). Our results have shown maximum diagnostic differentiation performance by using a minimum number of features. With our system, we have obtained an average accuracy of 92.02%, sensitivity of 92.59% and specificity of 91.46%, by using DT classifier with ten-fold cross validation.

Computer-aided diagnosis of diabetic subjects by heart rate variability signals using discrete wavelet transform method

The sensitivity of NASA-TLX scale, modified Cooper-Harper (MCH) scale and the mean inter-beat interval (IBI) of successive heart beats, as measures of pilot mental workload (MWL), were evaluated in a flight training device (FTD). Operational F/A-18C pilots flew instrument approaches with varying task loads. Pilots' performance, subjective MWL ratings and IBI were measured. Based on the pilots' performance, three performance categories were formed; high-, medium- and low-performance. Values of the subjective rating scales and IBI were compared between categories. It was found that all measures were able to differentiate most task conditions and there was a strong, positive correlation between NASA-TLX and MCH scale. An explicit link between IBI, NASA-TLX, MCH and performance was demonstrated. While NASA-TLX, MCH and IBI have all been previously used to measure MWL, this study is the first one to investigate their association in a modern FTD, using a realistic flying mission and operational pilots. Practitioner summary: NASA-TLX scale, MCH scale and the IBI were evaluated in a flight training device. All measures were able to differentiate most task conditions and there was a positive correlation between NASA-TLX and MCH scale. An explicit link between IBI, NASA-TLX, MCH and performance was demonstrated. Abbreviations: ANOVA: Analysis of Variance; ECG: Electrocardiograph; F/A: fighter/attack; ft: feet; FTD: flight training device; G: Gravity; km: kilometer; m: meter; m/s: meters per second; MWL: mental workload; MCH: modified cooper-harper; NASA-TLX: NASA Task Load Index; NM: Nautical Mile; NN: normal-to-normal; IBI: inter-beat interval; ILS: Instrument Landing System; RR: R-Wave to R-Wave; SD: standard deviation; TTP: tactics, techniques and procedures; WTSAT: Weapon Tactics and Situation Awareness Trainer.

Comparison of NASA-TLX scale, modified Cooper–Harper scale and mean inter-beat interval as measures of pilot mental workload during simulated flight tasks

A Review of Heart Rate Variability and its Applications

In this research an expert system for automated detection of abnormality of heart rate variability. For this purpose, a data acquisition system from Holter ECG records, is developed by using QRS detection techniques for the future development of an standard ECG database. New and innovative medical applications based on developments in the wireless networks field are being developed in the research as well as commercial sectors. This trend has just started and we predict wireless networks are going to become an integral part of medical solutions due to its benefits in cutting down healthcare costs and increasing accessibility for patients as well as healthcare professionals. In this research we give some background on applications of wireless networks in the heart rate variability processing and discuss the issues and challenges. We have also tried to identify some of the standards in use. Another contribution due to this paper is the identification of innovative medical applications of wireless networks developed or currently being developed in the research and business sectors in Vietnam.

Intelligent Heart Rate Variability Processing System

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A Review of Heart Rate Variability and its Applications

Intelligent Heart Rate Variability Processing System