Review of Heart Rate Variability Analysis Methods in Ergonomics Studies

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 (