Heart Rate Variability

Description

Heart Rate Variability (HRV) is an important measure of the variation in time between successive heartbeats and reflects the dynamic balance between sympathetic and parasympathetic nervous system activity. HRV assessment is widely used as a tool for assessing cardiovascular function and overall health.

HRV is influenced by various physiological factors that impact heart rate and variability between heartbeats, including breathing patterns, blood pressure regulation, and natural daily rhythms such as sleep-wake cycles, physical activity, and food intake. Together, these factors work to regulate the cardiovascular system, including heart rate and HRV.

The autonomic nervous system (ANS) plays a key role in regulating HRV by controlling heart rate and the balance between sympathetic and parasympathetic nervous system activity. The sympathetic nervous system is responsible for the "fight or flight" response to stress, which leads to an increase in heart rate and a decrease in HRV, while the parasympathetic nervous system is responsible for the "rest and digest" response, which leads to a decrease in heart rate and an increase in HRV.

HRV can be measured using techniques such as electrocardiography (ECG) and photoplethysmography (PPG). Factors like stress, physical activity, sleep, and medication use can all influence HRV, and the interplay between these factors is complex. Understanding the impact of these factors on HRV can provide insights into the health and function of the cardiovascular system and help guide interventions to improve overall health.

A value of 35.5 millisecond is considered healthy/normal.

Analysis of Accuracy & Reliability

The precise timing between heartbeats was calculated by measuring the time intervals between R-waves of successive beats (termed the R-R interval, Figure 6). The statistical variability in the length of successive intervals is termed heart rate variability (HRV). Certain HRV measures are closely associated with mental stress, and such measures were used to calculate the Mental Stress Index.

Heartbeats were then identified in blood flow signal extracted using BitDoctor technology. Rather than capturing electrical activity of the heart, blood flow signal characterizes arterial pressure pulses that propagate from the heart to the face with each beat of the heart. Advanced signal processing techniques are used to derive a robust blood flow signal. Such techniques include signal de-trending, de-noising and estimation of peaks and valleys (Figure 7).

This signal robustly tracks the activity of the heart in terms of number of beats and inter-beat timing. Similar to ECG, R-R intervals (Figure 8 - Left) were calculated from each participant’s blood flow information. Intervals between beats were measured from trough to trough of the fitted waves (Figure 8 – Right).

The accuracy of BitDoctor-based measurements relative to ECG-based measurements was then calculated. Only measurements with a positive signal-to-noise ratio were included.

Once the R-R intervals are determined, there are different methods to analyze heart rate variability (HRV). Here are three common ways:

1. Time-domain analysis:

A widely used method in heart rate variability (HRV) research, focusing on the examination of time intervals between successive heartbeats, known as interbeat intervals or R-R intervals, which are measured in milliseconds. This analysis provides valuable insights into the overall variability of the heart rate and the balance between sympathetic and parasympathetic nervous system activity. Additionally, a specific type of interbeat interval called the NN interval is of particular interest. The NN interval represents the interval between consecutive normal heartbeats, excluding any abnormal or ectopic beats. The analysis of NN intervals provides valuable insights into the variability of a normal heart rhythm. Table 4 presents some commonly used time-domain measures in HRV research, available through BitDoctor Technology along with their minimum required duration.

Table 4: Commonly used time-domain measures in heart rate variability (HRV) research.

2. Frequency-domain analysis:

Frequency-domain analysis is another method of HRV analysis that involves transforming the time-domain signal into its frequency components using mathematical techniques such as Fourier or wavelet transform. This method allows the identification and quantification of the variability in the different frequency bands of the HRV signal, which is related to the underlying physiological mechanisms involved in the regulation of the autonomic nervous system. Table 5 presents the most commonly used frequency-domain measures of HRV, available through BitDoctor Technology along with their minimum required duration.

Table 5: Commonly used frequency-domain measures of heart rate variability (HRV) obtained through mathematical techniques such as Fourier or wavelet transform.

3. Non-Linear Analysis:

Nonlinear HRV analysis is a sophisticated method of analyzing heart rate variability that utilizes complex mathematical algorithms to examine interactions among various components of the heart's autonomic control system. This approach involves the use of measures such as SD1, SD2, SD1/SD2, α, α1, and α2, which can be obtained from techniques such as Poincaré Plot and Detrended Fluctuation Analysis. Nonlinear HRV analysis can detect subtle changes in HRV and capture nonlinear relationships among different HRV parameters that may not be detected by traditional linear HRV analysis. By providing a more detailed and comprehensive view of HRV, nonlinear HRV analysis can aid in identifying early signs of cardiovascular disease, evaluating the effectiveness of interventions, and gaining insights into the underlying physiological mechanisms of HRV. A list of some of the measures used in nonlinear HRV analysis, available through BitDoctor Technology along with their minimum required duration can be found in Table 6.

Table 6: Features of Nonlinear HRV Analysis Based on Poincaré Plot and Detrended Fluctuation Analysis