Page 413 - Cardiac Nursing

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C HAPTER 1 7 / Heart Rate Variability 389
■ Figure 17-1 Example strip of ambulatory Holter ECG recorded during sleep. The beats are coded as Nor-
mal (“N”), and the R-R intervals (in milliseconds) for each overlapping pair of beats are displayed above and
slightly to the left of the R wave that terminates the interval. Two respiratory cycles of probable RSA are visually
apparent in the strip. Note that the measured sequential R-R intervals vary considerably within a few seconds for
this high HRV subject.
two successive normal beats, sometimes called an NN doublet The R-R intervals thus decoded from the raw ECG can be placed
(Fig. 17-1). into an ordered temporal sequence to form a time series, in which
The quality of HRV indices is ultimately dependent on the con- the continuous length of each cardiac cycle is an interval measure of
sistency of the basic measurement of each R-R interval. In modern time, usually reported in units of milliseconds(ms). Each R-R inter-
digital applications, the R-R interval is partially determined by the val may be inverted to a beat-specific instantaneous equivalent heart
sampling rate of the raw ECG, and also by characteristics of the R- rate, which can be considered the heart rate in beats per minute that
wave location finding algorithm. Typical sampling rates may vary would have been observed if all the heart beats in a 60-second period
from approximately 100 Hz (samples per second), still common in had exactly the length of that specific individual interval. 5
long time scale ambulatory monitoring, to 1,000 Hz or faster in
laboratory studies. In general, a higher digital waveform sampling HRV Measures
rate allows proportionally more precision in the estimation of the
location of the R waves at the cost of greater processing and mem- Although there are a variety of approaches used to analyze HRV,
ory requirements. However, the resulting apparent gain in precision the two major procedures are time domain analysis and frequency
may be illusory in ambulatory ECG recordings that contain noise domain analysis. Definitions for HRV measures based on these
and morphologies that vary slightly with posture and activity. approaches are presented in Tables 17-1 and 17-2, respectively.
Table 17-2 ■ DESCRIPTION OF FREQUENCY DOMAIN
MEASURES OF HRV
Table 17-1 ■ DESCRIPTION OF TIME DOMAIN MEASURES
OF HRV Measure Units Description
2
Measure Units Description PSD plot ms /Hz Plot of power spectral density (PSD) versus
frequency; frequency range is generally less
Mean RR ms Mean of all NN intervals than 0.4 Hz
SDNN ms Standard deviation of all NN intervals Total power ms 2 Area under PSD curve, equal to the variance
CoV Coefficient of variation, equal to of the segment; segment length can be short
100 SDNN/(mean RR) (5 minutes) or entire recording
SDANN ms Standard deviation of the averages LF ms 2 Power in the LF band between 0.04 and
of NN intervals in all 5-minute 0.15 Hz; it reflects both sympathetic and
segments of the recording parasympathetic activity
SDNN index ms Mean of the standard deviations of HF ms 2 Power in the HF band between 0.15 and
all NN intervals in all 5-minute 0.4 Hz; it predominantly reflects
segments of the recording parasympathetic activity
rmsSD ms Square root of the mean squared LF:HF Ratio of LF power to HF power; a higher
differences between successive NN number indicates increased sympathetic
intervals activity or reduced parasympathetic activity
pNN50 % Number of successive NN intervals LFnu % Low-frequency power in normalized units,
differing by more than 50 ms divided LF/(LF
HF), expressed as a percentage
by the total number of successive NN HFnu % High-frequency power in normalized units,
intervals, expressed as a percentage HF/(LF
HF), expressed as a percentage
ms, milliseconds. LF, low frequency; HF, high frequency.