Heart rate variability
[1] Methods used to detect beats include ECG, blood pressure, ballistocardiograms,[2][3] and the pulse wave signal derived from a photoplethysmograph (PPG).
ECG is considered the gold standard for HRV measurement[4] because it provides a direct reflection of cardiac electric activity.
[9] On the other hand, for patients having high blood pressure (hypertension), higher HRV is a risk factor for atrial fibrillation.
High-frequency (HF) activity has been found to decrease under conditions of acute time pressure and emotional strain[12] and elevated anxiety state,[13] presumably related to focused attention and motor inhibition.
[16] The neurovisceral integration is a model of HRV that views the central autonomic network as the decision maker of cognitive, behavioral and physiological regulation as they pertain to a continuum of emotion.
[26] Previous research has suggested that a large part of the attention regulation is due to the default inhibitory properties of the prefrontal cortex.
However, it places more emphasis on respiratory sinus arrhythmia and its transmission by a hypothesized neural pathway distinct from other components of HRV.
[37] Contribution of the respiratory rhythm to sinus arrhythmia in normal unanesthetized subjects during mechanical hyperventilation with positive pressure.
[citation needed] Factors that affect the input are the baroreflex, thermoregulation, hormones, sleep–wake cycle, meals, physical activity, and stress.
Activity in this range is associated with the respiratory sinus arrhythmia (RSA), a vagally mediated modulation of heart rate such that it increases during inspiration and decreases during expiration.
These patterns reflect highly sensitive physiological regulatory mechanisms that enable a healthy heart to adapt to various life influences.
To ensure accurate results therefore it is critical to manage artifact and RR errors appropriately prior to performing any HRV analyses.
[43][44] Robust management of artifacts, including RWave identification, interpolation and exclusion requires a high degree of care and precision.
The advantages of the nonparametric methods are (1) the simplicity of the algorithm used (fast Fourier transform [FFT] in most of the cases) and (2) the high processing speed.
[51] Analysis has shown that the LS periodogram can produce a more accurate estimate of the PSD than FFT methods for typical RR data.
Alternatively, to avoid artefacts that are created when calculating the power of a signal that includes a single high-intensity peak (for example caused by an arrhythmic heart beat), the concept of the 'instantaneous Amplitude' has been introduced, which is based on the Hilbert transform of the RR data.
Given the complexity of the mechanisms regulating heart rate, it is reasonable to assume that applying HRV analysis based on methods of non-linear dynamics will yield valuable information.
A new evaluation method has recently allowed to determine a HRV(HR) function with unprecedented precision:[67] it can be described by two descending exponential components for healthy individuals, in general.
[citation needed] Although cardiac automaticity is intrinsic to various pacemaker tissues, heart rate and rhythm are largely under the control of the autonomic nervous system.
In post-MI patients with a very depressed HRV, most of the residual energy is distributed in the VLF frequency range below 0.03 Hz, with only a small respiration-related variations.
In diabetic patients without evidence of autonomic neuropathy, reduction of the absolute power of LF and HF during controlled conditions was also reported.
In addition, a correlation between respiratory rate and the HF component of HRV observed in some transplanted patients also indicates that a nonneural mechanism may generate a respiration-related rhythmic oscillation.
In this condition characterized by signs of sympathetic activation such as faster heart rates and high levels of circulating catecholamines, a relation between changes in HRV and the extent of left ventricular dysfunction was reported.
In fact, whereas the reduction in time domain measures of HRV seemed to parallel the severity of the disease, the relationship between spectral components and indices of ventricular dysfunction appears to be more complex.
In particular, in most patients with a very advanced phase of the disease and with a drastic reduction in HRV, an LF component could not be detected despite the clinical signs of sympathetic activation.
Patients with chronic complete high cervical spinal cord lesions have intact efferent vagal neural pathways directed to the sinus node.
Flecainide and propafenone but not amiodarone were reported to decrease time domain measures of HRV in patients with chronic ventricular arrhythmia.
However, though the heart rate slowing in proportional to the (low) dose of atropine, the increase in HRV varies widely across and within individuals.
[85] The technique called resonant breathing biofeedback teaches how to recognize and control involuntary heart rate variability.
A randomized study by Sutarto et al. assessed the effect of resonant breathing biofeedback among manufacturing operators; depression, anxiety and stress significantly decreased.
