1. Heart Rate Variability
Soojin Park, MD
ICM+ Users Meeting
Singapore, ICP2013

2. Heart Rate Variability
Conventionally accepted term to describe variations in both
INTERVAL (oscillation in the interval between consecutive heart beats)
RATE (oscillations between consecutive instantaneous heart rates)

3. Predictive value of HRV
Congestive Heart Failure
Mortality after Myocardial Infarction
Mortality after Stroke
Fetal distress
Complications after Subarachnoid Hemorrhage

4. Autonomic modulation of the heart
RR interval or instantaneous HR generated by SA node
Main inputs to the SA node
Sympathetic nervous system (SNS)
Parasympathetic nervous system (PSNS)

5. Factors affecting input
Baroreflex
Thermoregulation
Hormones
Sleep-wake cycle
Physical activity
Stress
Medications (B-blockade, alpha-blockade)

6. Frequency-domain: interpretation
Power spectral density (PSD) shows how power (=variance) distributes as a function of frequency
Relative and total power fluctuates as a result of SNS and PSNS activity on SA node
Main spectral components:
High Frequency: Hz ( sec period)
Linked to PSNS activity
Low Frequency: Hz ( sec period)
Reflects both SNS and PSNS activity
Very Low Frequency
Probably not physiologic, reflects baseline and trends

7. Expected Fluctuations
Respiratory sinus arrhythmia
Vagally mediated (PSNS) – HF activity ( Hz)
Increases during inspiration
Decreases during expiration
Low-frequency oscillations
Associated with Traube-Hering-Mayer waves of BP
0.1 Hz or 10 sec period – (LF range Hz)

8. 3 methods of frequency analysis
Lomb-Scargle periodogram
Calculate spectrum of unevenly sampled RR
Fast Fourier Transform
Non-parametric, simplistic algorithm
Autoregressive
Parametric, smoother spectral components, easier post processing
Most of the time, comparable results.
FFT & AR can sometimes have conflicting results
LSP better for unevenly sampled RR

9. Frequency Analysis: A few details
Open .dta  HRV chart
Patient populations &
Abnormal beat detection
Detrending (FFT & AR)
Windowing (FFT)

10. Patient Population
Expected normal range influences parameters for abnormal beat detection
Neonates – expect much shorter beat
Experimental recording can be up to /min

11. Frequency Analysis: A few details
Open .dta  HRV chart
Patient populations &
Abnormal beat detection
Detrending (FFT & AR)
Windowing (FFT)

12. Advanced Detrending Method (smoothness priors)
PSD estimation assumes stationarity of data;
baseline trends contribute to nonstationarity
and cause distortion of HRV analysis
Tarvainen MP et al. An advanced detrending method with application to HRV analysis. IEEE Trans Biomed Eng. 2002;49(2):172-5.

13. Detrending FFT AR none linear Choose this detrending method
Open .dta  chart view  HRV chart
none
linear
Choose this
detrending method
to minimize VLF
bridge
smooth.
priors

14. Frequency Analysis: A few details
Open .dta  HRV chart
Patient populations &
Abnormal beat detection
Detrending (FFT & AR)
Windowing (FFT)

15. Window Function Applied to waveform prior to spectral analysis
Components in spectral analysis:
comparable strength with similar frequencies  disparate strength with dissimilar frequencies
If no window is used, leakage from large power components will occur
Leakage can interfere with ability to distinguish 2 frequencies within a waveform

16. Default window Open .dta  chart view  HRV chart blackman rectangular
hanning
kaiser
hamming
chebyshev
flat top
exponent
Default window
bartlett

17. Frequency-domain HRV: length of recording period
Spectral analysis of entire 24 hour period (as well as spectral results of shorter segments averaged over 24 hours) provide AVERAGES of the modulations attributable to the LF and HF components
Obscures detailed information about autonomic modulation of RR intervals available in shorter recordings
5 min windows are a good standard

18. Time-domain HRV (select)
SDSD: standard deviation of sequential differences in RR durations
RMSSD: square root of the mean squared differences of successive NN intervals
Short term variation
Estimates high frequency variations in HR

19. Using ICM plus to evaluate HRV in the Time Domain

20. Time-domain HRV: length of recording period
Dependent on length of recording period
Must be standardized
5 min
24 hour

22. LF/HF slope during the first 3 days was a significant predictor of secondary complications after SAH
LF component (sympathetic) increased over 3 days in pts with complications
HF component (vagal) decreased over 3 days in pts with complications
LF/HF ratio increased over 3 days in pts with complications
N=30, excluded pts with prior cardiac disease or autonomic dysfunction, analysed 5 min stationary ECG segments between
Method
Ectopic-free data detrended and resampled
Estimated power spectral density of each 5min recording using FFT
Quantified power spectra into standard frequency-domain measurements (VLF, LF, HF power)  normalized units (each power component divided by total power minus VLF components)  determined mean value of each parameter from three 5min segments from each day
Identified trend of HRV changes within each patient (rather than btwn patients): Compared each daily mean power component against baseline value (parameter from day 1)  amount of change from day 1 to day 3 in each parameter estimated using least squares regression line  slopes
Caution: same pattern seen in acute MI; HF is significantly related to respiratory sinus rhythm (while these pts were mechanically ventillated with RR btwn 10-18, fundamental respiratory frequency (FRF) was not controlled for

23. High Grade aSAH pt who developed Takotsubo cardiomyopathy Symptomatic after neosynephrine started, with increased WOB, prompting a repeat TTE (EF 60%10%) RMSSD analysis, showing decrease in HRV preceding respiratory distress (and discovery of CM by several hours)
Coefficient of Variation = SD/mean
Park S, Kaffashi F, Loparo KA, Jacono FJ. The use of HRV
for the early detection of treatable complications after aSAH.
JCMC. E-pub 25 April 2013