1. Heart Rate Variability for Clinicians
Phyllis K. Stein, Ph.D.
Research Associate Professor of Medicine and Director, Heart Rate Variability Laboratory
Washington University School of Medicine
St. Louis, MO

2. The Big Picture
Heart rate is under both sympathetic (SNS) and parasympathetic (PNS) control.
Rapid fluctuations in heart rate usually reflect PNS control only (respiratory sinus arrhythmia).
Therefore, changes in heart rate over time provide a window onto autonomic physiology.
Analysis of heart rate variability (HRV) quantifies these changes over time.

3. Renin angiotensin
system
Heart Rate
Cardiac output
Blood pressure
Parasympathetic
Nervous system
Sympathetic
Simplified model of cardiovascular control showing modulation of heart rate by parasympathetic and sympathetic feedback loops

4. Influences on HRV Extrinsic Rhythms Activity Sleep-wake cycle
Eating Meals
Mental Stress
Physical Stress

5. Influences on HRV Intrinsic Periodic Rhythms
Respiratory sinus arrhythmia
Baroreceptor reflex regulation
Thermoregulation
Neuroendocrine secretion
Circadian rhythms
Sleep stages during the night
Other, unknown rhythms

6. Quantifying HRV Approach 1: The Physiologist’s Paradigm
HR data collected over short period of time (~5-20 min), with or without interventions, under carefully controlled laboratory
conditions.
Approach 2: The Clinician’s and the Epidemiologists’s Paradigm
Ambulatory Holter Recordings collected over 24-hours or less, usually on outpatients.

7. Time Domain and Geometric Analyses Frequency Domain Analysis
Quantifying HRV
Approach 1: How much variability is there?
Time Domain and Geometric Analyses
Approach 2: What are the underlying rhythms? What physiologic process do they represent? How much power does each underlying rhythm have?
Frequency Domain Analysis
Approach 3: How much complexity or self-similarity is there?
Non-Linear Analyses

8. Time Domain HRV
Calculated from normal-to-normal interbeat (N-N) intervals
SDNN-Standard deviation of N-N intervals in ms (Total HRV)
SDANN-Standard deviation of 5- min mean values of N-Ns for each 5 minute interval in ms (Circadian HRV)
Both reflect “longer-term” HRV

9. Time Domain HRV
SDNNIDX-Average of standard deviations of N-Ns for each 5 min interval in ms (Combined sympathetic and parasympathetic HRV)
Reflects “intermediate-term” HRV
AVGNN-Average of N-N intervals in ms
Equivalent to heart rate of normal-to-normal beats. (HR=60,000/AVNN)

10. Time Domain HRV
Calculated from differences between successive N-N intervals
rMSSD-Root mean square of successive differences of N-N intervals in ms
pNN50-Percent of successive N-N differences >50 ms
Referred to as “short-term” HRV and reflect parasympathetic influence on heart rate

11. Frequency Domain HRV
Based on autoregressive techniques or fast Fourier transform (FFT).
Partitions the total variance in heart rate into underlying rhythms that occur at different frequencies.
These frequencies can be associated with different intrinsic, autonomically-modulated periodic rhythms.

12. What are the Underlying Rhythms?
One rhythm
5 seconds/cycle or
12 times/min
5 seconds/cycle=
1/5 cycle/second
1/5 cycle/second=
0.2 Hz

13. What are the Underlying Rhythms?
Three Different Rhythms
High Frequency = 0.25 Hz (15 cycles/min
Low Frequency = 0.1 Hz (6 cycles/min)
Very Low Frequency = Hz
(1 cycle/min)

14. What are the Underlying Rhythms in the Heart Rate Signal?
High frequency band (HF)
At respiratory frequencies (9-24 cycles/minute)
PNS influences on HR
Low frequency band (LF)
At Mayer wave frequencies~every sec frequency
Baroreceptor, SNS, PNS influences on HR

15. What are the Underlying Rhythms in the Heart Rate Signal?
Very low frequency band (VLF) At ~every 20 sec-every 5 min frequency
Reflects vasomotor changes, thermoregulatory, possibly PNS influences on HR
Ultra low frequency power band (ULF)
At >every 5 min to once in 24 hours
Reflects circadian, neuroendocrine, activity, other unknown rhythms

16. 24-hour average of 2-min power spectral plots in a healthy adult
LF peak
HF peak
0.20 Hz
0.40 Hz
24-hour average of 2-min power spectral plots in a healthy adult

17. Non-Linear HRV
Non-linear HRV characterize the structure of the HR time series, i. e., is it random or self-similar.
Increased randomness of the HR time series is associated with worse outcomes in cardiac patients.

18. Non-Linear HRV
Most commonly used measure of randomness is the short-term fractal Scaling exponent (DFA1). Decreased DFA1  increased randomness of the HR.
Another index is power law slope, a measure of longer term self-similarity of HR. Decreased slope  worse outcome.
Normal DFA1 is about DFA1<0.85 is associated with higher risk.

19. Comparison of Normal and Highly Random HRV Plots

20. Clinical Correlates of Decreased HRV
Inducible VT and VF
Risk of Sudden Cardiac Death
Increased mortality post-MI
Congestive heart failure
Poorer survival in CHF
COPD

21. Clinical Correlates of Decreased HRV
Diabetic neuropathy
Alcoholic neuropathy
Post cardiac transplant
Depression
Susceptibility to SIDS
Poor survival in premature babies
Increased mortality in population studies

22. Pitfalls in Understanding HRV
HRV reflects phasic not tonic modulation of HR and cannot measure autonomic tone, just modulation of HR.
HRV is a “black box.” Decreased HRV can mean decreased autonomic input or decreased cardiac responsiveness.
HRV cutpoints for risk stratification best established in immediate post-MI period.

23. Pitfalls in Understanding HRV
Association is not causation
Endurance athletes have high HRV.
Does exercise training cause increased HRV, or are people with high HRV more suited to endurance exercise?
HRV can be exaggerated by increased randomness, which is not a truly normal sinus rhythm despite the normal appearance of the ECG.

24. Clinical Implications
Normal HRV is a good negative risk stratifier.
Patients with preserved HRV (SDNN>100 ms) are low risk in the absence of other significant risk factors.
Sensitivity and specificity of decreased HRV (SDNN<70 ms) for immediate post-MI is about 30%.

25. How Can HRV Be Increased?
Medications
Beta-blockers
ACE inhibitors in CHF
Digoxin?
Ventricular resynchronization therapy
Cardiac rehab?

26. How Can HRV Be Increased?
Lifestyle modifications:
Smoking cessation
Weight loss
Exercise
Stress management
Lipid lowering?
Glycemic control

27. Final Thoughts
Analysis of HRV is a non-invasive method for identifying abnormalities in cardiac autonomic modulation.
Decreased HRV by itself is not sufficient to risk stratify patients, e.g. for AICD implantation, but HRV in combination with other risk stratifiers improves risk stratification.

28. Final Thoughts
Normal HRV identifies patients at lower risk for events.
HRV cutpoints are population-specific and have not been fully elucidated.
It now appears that decreased HRV and increased randomness of HR are independently associated with higher risk.