Iran Occupational Health

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Introduction and aim: Electrocardiography is one of the most useful and common tools for examining heart function. In studies in the 1960s, heart rate variability (HRV) was introduced and used as a non-invasive tool to evaluate the functions of the autonomic nervous system and other functional disorders of the heart under various conditions. In addition to the clinical applications of heart rate variability, this physiological parameter is also widely used in ergonomics and occupational health. In the study of mental workload, exposure to industrial noise, shift work, stress, and other parameters related to the workplace benefit heart rate variability. Heart rate variability is regulated by the autonomic nervous system and by the sinoatrial (SA) node. The autonomic nervous system is divided into sympathetic and parasympathetic branches, the autonomic nervous system is divided into sympathetic and parasympathetic branches, thereby affecting the heart rate and heart rate variability. Sympathetic activity tends to increase heart rate and decrease heart rate variability, while parasympathetic tend to reduce heart rate and increase heart rate variability. The most prominent component of the variable heart rate period is a respiratory arrhythmia (RSA), which is considered to be 0.15 to 0.4 Hz. The high-frequency component is only affected by parasympathetic neural activity. Another component of heart rate variability is the low-frequency (LF) component in the frequency range, 0.04 to 0.15 Hz. This component is affected by sympathetic and parasympathetic neural activity. Many computational methods have been developed for HRV analysis, each of which is associated with strengths and weaknesses. Although there may still be difficulties in interpreting this index, it provides researchers and health experts with reliable non-invasive physiological information. HRV parameters can be classified in terms of time, frequency, and nonlinear methods. The commercially available ECG equipment does not usually include HRV features due to the lack of standard diagnostic protocols. As an alternative to this commercial software, many simple, online and free, device-independent, and portable software has been developed for HRV analysis and cardiovascular research.
Heart Rate Variability Quantification: Heart rate variability can be measured in long-term (24 hours), short-term (5 minutes), and very short-term periods (less than 5 minutes) and can be analyzed as time- -domain, frequency-domain, and non-linear.
1. Time-domain measures
The simplest method to analyze heart rate variability is time-domain. Analysis of heart rate variability in the time-domain is performed through both statistical and geometric analysis, both of which are based on heart rate or RR intervals between successive QRS series. Using these methods, the heart rate can be assessed at any point in time or between normal consecutive sets. Statistical parameters recommended by the European Society of Cardiology and the American Society of Pacing Electrophysiology include: 1) SDNN 2) NN50 3) SDSD 4) SDANN 5) RMSSD 6) SDSD 7) NN50 count (Table 1).
Table 1: time-domain measures of HRV

Variable	Units	Description
[1]SDNN	ms	Standard deviation of all NN intervals
SDRR[2]	ms	standard deviation of RR interval series
SDANN	ms	Standard deviation of the averages of NN intervals in all 5 min segments of the entire recording
SDNN index	ms	Mean of the standard deviations of all NN intervals for all 5 min segments of the entire recording
pNN50	%	NN50 count divided by the total number of all NN intervals
HR Max –HR Min	bmp	The average difference between the highest and lowest HRs during each respiratory cycle
rMSSD	ms	Root mean square of successive RR interval difference
HRV triangular index		Integral of the density of the RR interval histogram divided by its height
TINN	ms	Baseline width of the RR interval histogram

 
2. Frequency-domain measures:
Time-domain measurements information about the overall change in time series or maximum variable amplitude, but no information about periodic heart rate fluctuations. Data frequency domain analysis provides information on how power distribution is a function of frequency. According to the European Society of Cardiology, the power range of a healthy person is usually divided into four main frequency bands. The range of components used is usually: high-frequency (0.4 - 0.15 Hz), low-frequency (0.15-0.04 Hz), very low frequency (0.04 - 0.003 Hz) and infinitely low frequency (<0.003 Hz).
Table 2: frequency-domain measures of HRV

Variable	Units	Description
ULF power	ms2	Absolute power of the ultra-low-frequency band (≤0.003 Hz)
VLF power	ms2	Absolute power of the very-low-frequency band(0.0033–0.04 Hz)
LF peak	Hz	Peak frequency of the low-frequency band (0.04–0.15 Hz)
LF power	ms2	Absolute power of the low-frequency band (0.04–0.15 Hz)
LF power	nu	Relative power of the low-frequency band (0.04–0.15 Hz) in normal units
LF power	%	Relative power of the low-frequency band (0.04–0.15 Hz)
HF peak	Hz	Peak frequency of the high-frequency band (0.15–0.4 Hz)
HF power	ms2	Absolute power of the high-frequency band (0.15–0.4 Hz)
HF power	nu	Relative power of the high-frequency band (0.15–0.4 Hz) in normal units
HF power	%	Relative power of the high-frequency band (0.15–0.4 Hz)
LF/HF	%	Ratio of LF-to-HF power

 
3. Nonlinear measures:
The parameters commonly used in the analysis of time and frequency domain HRV are not always suitable for analysis due to the existence of various nonlinear phenomena in physiological signal parameters. Therefore, the use of nonlinear techniques is recommended. Pincus developed Approximate Entropy (ApEn) as a nonlinear complexity index to determine the random quantity of a physiological time series. Richman and Moorman developed a sample entropy (SampEn), a new family of statistics, to measure the complexity and data of clinical and experimental time series, and compared it to ApEn.
Common heart rate analysis tools: HRV analysis has led to the development of several commercial and non-commercial software. Most commercial equipment for heart monitoring and HR analysis includes software that depends on devices for HRV analysis, but there are also software-independent. In addition to commercial tools, several free non-commercial software have been developed.
Kobius:
This powerful software is based on MATLAB. Kobius (version 2.1) is a free and non-commercial software for researchers and physicians (http://kubios.uef.fi 7). This software can analyze HRV in time-, frequency-domine, and non-linear indicators. Kobius is compatible with Windows and Linux operating systems, and supports both ECG and RR data formats, and performs the pre-processing operations required to detect QRS and correct corrections.
gHRV:
gHRV is based on the Python programming language and performs heart rate variability analysis for a specified period of time. The software can be easily run on Windows, Apple OS X Linux, or GNU operating systems. Data and formats supported by gHRV include heart rate in WFDB and ASCII formats, (IBI (InterBeat Intervals ASCII files), heart rate monitoring in polar (common heart rate recording form), and Santo wristbands. The pre-processing step involves deleting and interpolate. Updated versions of gHRV are available for free at http://ghrv.milegroup.net. This website provides information on how to download, install, and use. GHRV analyzes HRV in terms of time, frequency, and nonlinear domain.
KARDIA:
KARDIA is based on MATLAB programming language. All functions of this software are written in a single program (kardia.m) which is available for free (at http://sourceforge.net/projects/mykardia/). Execution of m-file requires MATLAB 7 or updated version with MATLAB signal processing toolbox. KARDIA can calculate the heart rate in any method sampled by the user by interpolating using fixed, linear, or splint methods. Linear parameters of heart rate variability in time and frequency range and nonlinear parameters can be quantified by analyzing fluctuations.
VARVI:
VARVI is free software designed in Python programming language for HRV analysis in response to various visual stimuli. This HRV software measures the person who is watching a video. This software has wide applications in psychiatry and psychological studies. VARVI software is available at varvi.milegroup.net.
RHRV:
This software is based on R programming language for HRV statistical calculations. This programming language (R) is originally implemented in the S language and is compatible with Windows and MacOSX. Several advantages of this software are available. The data file containing the heart rate in WFDB and ASCII format is entered into the software. Software package is available for download and installation.
ARTiiFACT :
ARTiiFACT is a MATLAB-based tool with compiler 4.13 for artifact processing and HRV analysis. ARTiiFACT has options for processing a set of data, analyzing HRV by the autoregressive and nonlinear domain model, as well as correcting respiration-related distortions, which allows raw data from the ECG to be applied bypassing the filter over, and over. Set their bypass at the appropriate critical frequency. Software, tutorials and user manuals are available for free upon request (email tobias.kaufmann@uni-wuerzburg.de).
LabVIEW:
LabVIEW software is a design-specific operating system that provides a development environment. It can also be used to analyze heart rate variability. LabVIEW is supported by Windows, Linux GNU or Apple OSX.
POLYAN:
POLYAN is based on MATLAB programming language and has a computing environment with the aim of excellent performance in numerical and visual calculations. The software can be easily run on Windows operating systems, Linux GNU or Apple OS X. POLYAN is a free software specifically designed for the simultaneous analysis of multiple signals (multi-parameter approach) to evaluate the performance of an Autonomic nervous system.
aHRV:
aHRV is a commercial software developed by Nevrokard for heart rate variability analysis. The software supports data in the form of ASCII, binary files in European data format and many dedicated formats.
Conclusion:
HRV plays an important role in assessing ANS fluctuations in healthy individuals and heart patients and promoting understanding of disease mechanisms and physiological phenomena. Currently, there are commercial or semi-commercial tools that possible simultaneous ECG recording, respiration, blood pressure. Commercially available recording systems are not usually separated by HRV analysis features. Software developers have not prioritized this because HRV is not yet included in standard diagnostic protocols. However, various developers and research groups are developing software tools tailored to their specific needs.

Keywords: Heart Rate Variability, Autonomic nervous system, Heart Rate, Analysis Method

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