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.

BitDoctor Technology is designed to provide quick HRV measurements in just 30 seconds, but it's important to recognize that HRV analysis is typically performed over much longer periods of time to obtain accurate results. In general, electrocardiograms of at least 240-300 seconds are needed to reliably measure HRV. While short-term HRV measurements such as SDNN and RMSSD can be a useful approximation of longer-term HRV, there are some limitations. RMSSD is generally considered to be a more reliable measure than SDNN and averaging multiple 10- second ECG recordings can help to improve accuracy. However, it's important to use caution when interpreting the results of BitDoctor Technology, as it is subject to limitations similar to other short-term HRV analysis methods.

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.

Figure 6: The R-R Interval

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).

Figure 7: RRI estimation algorithm in BitDoctor signals

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).

Figure 8: (Left) R-R interval in BitDoctor signals. (Right) The R-R Interval Signal extracted from ECG.

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.

Signal Point	Unit	Feature Description	Available after (sec)
Mean HR	bpm	Average heart rate, expressed in beats per minute	30
Mean RRI	ms	Average RR interval, expressed in milliseconds	30
SDNN	ms	Standard deviation of NN intervals, expressed in milliseconds	30
NN50		Number of adjacent NN intervals that differ from each other by more than 50 ms	60
pNN50	%	Percentage of adjacent NN intervals that differ from each other by more than 50 ms	60
RMSSD	ms	s Root mean square of successive RR interval differences, expressed in milliseconds	30

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.

Signal Point	Unit	Feature Description	Available after (sec)
VLF power	ms2	Power measured in the very-low-frequency band (0.003-0.04 Hz), expressed in sum of power	120
LF power	ms2	Power measured in low-frequency band (0.04-0.15 Hz), expressed in sum of power	120
HF power	ms2	Power measured in high-frequency band (0.15-0.40 Hz), expressed in sum of power	120
Total Power	ms2	Summation of power measures in all frequency bands, expressed in sum of power
LF peak	Hz	Highest amplitude frequency in the low-frequency band (0.04-0.15 Hz) using Welch’s power spectral density estimate	120
HF peak	Hz	Highest amplitude frequency in the high-frequency band (0.15- 0.40 Hz) using Welch’s power spectral density estimate	120
LF power	nu	Proportion of power in the low-frequency band (0.04-0.15 Hz) to the summation of powers in low- and high-frequency bands, expressed in normal units (nu)	120
HF power	nu	Proportion of power in the high-frequency band (0.15-0.40 Hz) to summation of powers in low- and high-frequency bands, expressed in normal units (nu)	120
LF/HF		Ratio of power in low-frequency band (0.04-0.15 Hz) to power in high-frequency band (0.15-0.40 Hz)	120
LF_AR peak	Hz	Highest amplitude frequency in the very-low-frequency band (0.003-0.04 Hz) using Autoregressive power spectral density estimate — Burg’s method	120
HF_AR peak	Hz	Highest amplitude frequency in the high-frequency band (0.15- 0.40 Hz) using Autoregressive power spectral density estimate — Burg’s method	120

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.

Signal Point	Unit	Feature Description	Available after (sec)
SD1	ms	Standard deviation of points perpendicular to the line of identity on the Poincaré Plot, expressed in milliseconds	120
SD2	ms	Standard deviation of points along the line of identity on the Poincaré Plot, expressed in milliseconds.	120
SD1/SD2		Ratio of SD1 to SD2 (both obtained from Poincaré Plot)	120
α		Scaling exponent of RR intervals over different time series based on Detrended Fluctuation Analysis	120
α1		Short-term fluctuations from Detrended Fluctuation Analysis	120
α2		Long-term fluctuations from Detrended Fluctuation Analysis	120

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