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Heart Rate Variability analysis methodology


Methods of HRV Analysis

Short-term HRV analysis requires much shorter recordings - typically 5-min long. However such recordings are assumed to be done at steady-state physiological condition and should be properly standardized to produce comparable data. Typically such measurements should be done in either supine or comfortably sitting relaxed position, limiting body movements, conversations, any mental activities.

According to the standards set forth by the Task Force of the European Society of Cardiology and North American Society of Pacing and Electrophysiology in 1996, there are two methods of analysis of HRV data: time-domain and frequency-domain analysis. In either method, the interbeat intervals should be properly calculated and any abnormal heartbeats found.
Time-domain measures are the simplest parameters to be calculated. Before such calculation all abnormal heartbeats and artifacts must be removed from consideration. The following time-domain parameters can be calculated for both long-term and short-term recordings: Mean HR, SDNN and RMS-SD. Some extra parameters can be calculated specifically for long-term recordings. The time-domain parameters are associated mostly with overall variability of HR over the time of recording, except RMS-SD, which is associated with fast (parasympathetic) variability.
Mean NN (beat by beat interval in ms) RMS-SD (ms)
The example screenshots above were taken from Heart Rhythm Scanner which is the one of the Heart Rate Variability Systems available on the market today. Frequency-domain measures pertain to HR variability at certain frequency ranges associated with specific physiological processes. Before frequency-domain analysis is performed, all abnormal heartbeats and artifacts must be detected and removed, then cardiotachogram (sequence of RR intervals) must be re-sampled to make it as if it is a regularly sampled signal. A standard spectral analysis routine is applied to such modified recording and the following parameters evaluated on 5-min time interval: Total Power (TP), High Frequency (HF), Low Frequency (LF) and Very Low Frequency (VLF). When long-term data is evaluated an additional frequency band is derived - Ultra Low Frequency.
LF/HF ratio SD (standard deviation of all NN intervals)
The HF power spectrum is evaluated in the range from 0.15 to 0.4 Hz. This band reflects parasympathetic (vagal) tone and fluctuations caused by spontaneous respiration known as respiratory sinus arrhythmia.

The LF power spectrum is evaluated in the range from 0.04 to 0.15 Hz. This band can reflect both sympathetic and parasympathetic tone.

The VLF power spectrum is evaluated in the range from 0.0033 to 0.04 Hz. The physiological meaning of this band is most disputable. With longer recordings it is considered representing sympathetic tone as well as slower humoral and thermoregulatory effects. There are some findings that in shorter recordings VLF has fair representation of various negative emotions, worries, rumination etc.

The TP is a net effect of all possible physiological mechanisms contributing in HR variability that can be detected in 5-min recordings, however sympathetic tone is considered as a primary contributor.

The LF/HF Ratio is used to indicate balance between sympathetic and parasympathetic tone. A decrease in this score might indicate either increase in parasympathetic or decrease in sympathetic tone. It must be considered together with absolute values of both LF and HF to determine what factor contributes in autonomic disbalance.

The frequency domain analysis is traditionally performed by means of Fast Fourier Transformation (FFT). This method is simple in calculation but for fair representation of all frequency-domain HRV scores at least 5-min data should be collected. FFT assumes that time series represents a steady-state process. Because of that all data recordings should be conducted at highly stable standardized conditions, when no other factors other than current autonomic tone contributes in HRV. One of the most serious disadvantages of that is its insensitivity to rapid transitory processes, which often possess very valuable information about how physiology or certain pathological processes behave dynamically.

Some most recent studies implemented an alternative way to estimate power spectrum of HRV. It is based on auto-regression methods. One of its major advantages is that it doesn't require to have analyzed data series to be in steady state. Thus any HRV data can be analyzed and fair HRV information still derived. Such analysis can be also performed on relatively shorter time intervals (less than 5 minutes) without missing meaningful HRV information. Finally this method is sensitive to rapid changes in HR properly showing tiny changes in autonomic balance. The drawback of this approach is a necessity to perform massive calculations to find best order of auto-regression model.

Normative Data Sets

From clinical perspective it is important not only to evaluate all HRV scores but be able to assess such HRV data, whether they are normal or not and how to interpret such data. It is known that HRV scores are age-dependent. Most of scores decrease with age. For better HRV data assessment special sets of reference ranges for each HRV parameter were created. Such ranges are based on statistics derived from HR data measured in a number of healthy individuals of different ages under standardized conditions. Such norms are considered as a reference point and cannot be used for any diagnostic purpose.

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