Page 414 - Cardiac Nursing

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390 P A R T III / Assessment of Heart Disease
Time Domain Analysis periods and specialized methods of analysis. In addition, the
Time domain analysis is based on the statistical interpretation of clinical interpretation of findings in these frequency ranges re-
R-R time interval values. Time domain measures of HRV (Table mains controversial. 3
17-1) are closely related to the total variance of the heart signal. 3,8 In common with other variance-like measures, the within-
The most common index of overall HRV is the standard deviation subject HRV band power estimates are often reexpressed using the
of all R-R intervals (SDNN), typically involving 80,000 to natural logarithm transform to reduce distributional skewness be-
150,000 heart period values in a 24-hour recording. Long-term fore use in statistical procedures. HRV quantitative band power
variability, such as that reflecting normal circadian influence over summary indices (LF, HF, etc.) computed using the AR or DFT
a 24-hour period, is best reflected by two measures based on par- methods should be virtually identical. 12
titioning the full recording into sequential 5-minute segments. Several derived measures can easily be computed from these spec-
Each segment typically contains 300 to 500 R-R intervals, and tral band summaries (see Table 17-2). Normalized variants of the LF
there would be 288 such segments in a 24-hour recording. The and HF indices are often defined by dividing the power in each band
SDANN is defined as the standard deviation of the means of the by the total power, with the result expressed as a percentage. 13
R-R intervals in each 5-minute segment, whereas the comple- It should be pointed out that the HRV spectrum and
mentary SDNN index is the mean of the standard deviations of spectrum-based band power (variance) summary statistics, like all
the R-R intervals in each 5-minute segment. HRV measures, are defined over blocks of R-R intervals; thus,
Short-term time domain measures of HRV are derived from their meaning is not localized to a particular instant in time or to
the differences of successive normal R-R intervals. They are highly a particular beat. Typical block window lengths in clinical and re-
correlated and are considered to provide good estimates of PSNS search applications range from 2 minutes to 24 hours. Spectra de-
3
activity. Short-term measures include the square root of the mean rived from shorter blocks are more localized in time and are more
squared difference of successive normal R-R intervals (rmsSD) likely to be internally stationary but may have less frequency res-
and the percentage of successive normal R-R intervals that change olution, especially with respect to slower rhythm patterns. HRV
by more than 50 milliseconds compared with the total number of spectra based on very long individual blocks (e.g., 24 hours) will
R-R intervals (pNN50). have the ability to resolve very slow rhythmic patterns but will al-
most certainly span nonstationary data segments and heteroge-
Frequency Domain Analysis neous latent autonomic states.
Frequency domain analysis, or spectral analysis, is an elegant
method for studying the rhythmic components in an R-R interval
sequence and presents intriguing possibilities for disentangling HRV PATTERNS IN COMMON
9
PSNS and SNS influences on the heart. A plot of the power spec-
tral density of HRV versus frequency describes how the variances CARDIOVASCULAR CONDITIONS
of the frequency components of the heart signal are distributed. 3
Both parametric and nonparametric methods common to time The following section provides a summary of HRV research find-
series analysis have been used to estimate the power spectral den- ings in myocardial infarction (MI); arrhythmias and sudden
sity. The most common methods are the discrete Fourier transform death; angina; hypertension (HTN); heart failure; and cardiac
(DFT) (nonparametric) and autoregressive (AR) (parametric) time surgery, heart transplant, and other invasive procedures. The
series models. The AR model-based spectrum is usually less com- reader should refer to related chapters for more detailed descrip-
putationally efficient than the DFT, but it can be applied to data tions of these conditions and interventions.
sequences of arbitrary length, including very short segments. The
AR approach tends to produce a spectrum that is statistically more Myocardial Infarction
stable than that produced by the DFT but requires assumptions
about the time series model. 3 It is well established that HRV patterns are disturbed in patients
The total area under the curve of the power spectral density who have experienced MI. 14–17 In post-MI patients, those with
versus frequency plot is equal to the total statistical variance, or restrictive left ventricular filling have been reported to have espe-
the power of the signal. These power (variance) distributions are cially reduced HRV patterns compared with those without this
calculated for defined frequency bands and are interpreted as an disorder. 18
estimate of the variance of the HRV signal within that band Decreased HRV after MI is viewed as a significant risk factor
(Table 17-2). There are two major spectral components seen in for cardiac death 16,17 or subsequent nonfatal MI within 12
HRV data: the high-frequency (HF) (0.15–0.40 Hz) component months. 19 HRV measures of total variability, such as SDNN and
and the low-frequency (LF) (0.04–0.15 Hz) component (Fig. 17-2). SDANN, are viewed as the most useful predictors of mortality. 3,16
The HF component is associated with respiration 8,10 and is con- Erratic sinus rhythms (sinus arrhythmia of nonrespiratory origin)
sidered to reflect the relative input of the PSNS. The basis of the as identified by abnormal Poincaré plots in post-MI patients are
LF component is more controversial and may be the result of both significant risk markers for increased mortality at follow-up. 17 Re-
11
SNS and PSNS activity input. The LF to HF ratio (LF:HF) has sults from a landmark study, the Multicenter Post-Infarction Project,
been regarded as reflecting the balance between the mixed PSNS indicate that patients with an SDNN of less than 50 milliseconds
3
and SNS activity input to the PSNS activity input. The spectral (24-hour recording), measured within 11 days of the MI, have a
HF and the LF:HF are often reported together in nursing re- risk of mortality at 1 year that is 5.3 times higher than do patients
search studies seeking to explore the joint contribution of the with an SDNN greater than 100 milliseconds. 20 Predicted risk is
SNS and the PSNS branches to HRV phenomena. Studies of also increased for patients with below-normal SDNN and
very low-frequency and ultra-low-frequency ranges have also SDANN values in the chronic phase after MI. Reported normal
been conducted but require long uninterrupted sampling lower limits measured in patients, at least 3 months after MI,