How can you evaluate heart rate variability (HRV) on a polysomnogram (PSG)?

Evaluating Heart Rate Variability on a Polysomnogram

Heart rate variability (HRV) on a polysomnogram can be evaluated using both time-domain and frequency-domain analyses to assess autonomic function, with the most reliable measurements obtained during slow-wave sleep when parasympathetic activity is most stable. 1

Time-Domain Analysis Methods

• R-R Interval Measurements:

  • Calculate the difference between the longest and shortest R-R intervals
  • Standard deviation of 5-minute average of normal R-R intervals (SDANN)
  • Root-mean square of the difference of successive R-R intervals (rMSSD)
  • For longer recordings (24-hour), calculate pNN50 (percentage of adjacent NN intervals that differ by more than 50 ms) 1

• Reliability Considerations:

  • Heart rate measurements show good reproducibility within and across nights in all sleep stages 2
  • Time-domain measures primarily reflect parasympathetic activity 1
  • Most reliable during slow-wave sleep (SWS) due to stable autonomic state 2, 3

Frequency-Domain Analysis Methods

• Spectral Power Components:

  • High-frequency (HF) power: 0.15-0.45 Hz - reflects parasympathetic activity 4
  • Low-frequency (LF) power: 0.04-0.15 Hz - reflects mixed sympathetic and parasympathetic influences 4
  • Very low-frequency (VLF) and ultra-low frequency (ULF) components can also be analyzed 1

• Interpretation Guidelines:

  • HF component is synchronized with respiration and primarily reflects vagal tone 4
  • LF/HF ratio serves as a relative indicator of sympathovagal balance 4
  • HF power shows high reliability during SWS (r = 0.765, P < 0.001) 3
  • LF power and LF/HF ratio show poorer reproducibility across all sleep stages 2

Sleep Stage Considerations

• Optimal Recording Conditions:

  • SWS provides the most stable and reproducible state for HRV assessment 2, 3
  • SWS can be identified using HRV patterns with approximately 87% accuracy 2
  • During SWS, parasympathetic activity predominates, creating stable conditions for HRV measurement 1

• Sleep Stage Effects:

  • HRV parameters vary significantly between sleep stages 3
  • REM sleep shows higher sympathetic activity and more variable HRV 3
  • Non-REM sleep, especially SWS, shows higher parasympathetic predominance 4

Technical Considerations

• Signal Processing Requirements:

  • Ensure accurate R-wave peak identification with proper temporal accuracy 1
  • Filter motion artifacts and ectopic beats that can distort HRV measurements 1
  • Use a minimum of 5-minute segments for reliable frequency domain analysis 4
  • Consider circadian rhythms and patient activity when interpreting 24-hour recordings 1

• Potential Pitfalls:

  • Interpretation becomes difficult when overall variability is very low (as in severe heart failure) 4
  • The absolute power in low-frequency band should not be used as a direct index of sympathetic activity 1, 4
  • Respiratory patterns affect HRV and should be considered in interpretation 4
  • Age significantly affects HRV magnitude and should be factored into analysis 4

Clinical Applications

• Risk Stratification:

  • Reduced HRV (especially time-domain measures) is associated with increased mortality risk 1
  • Lower LF/HF ratio and lower LF are associated with higher atrial fibrillation incidence 5
  • HRV during sleep can be used in cardiovascular risk assessment, especially in older adults 6, 3

• Sleep Disorder Assessment:

  • HRV patterns differ between patients with and without sleep-disordered breathing 7
  • Can help identify autonomic dysfunction in various sleep disorders 6
  • May be used to screen patients for possible referral to sleep laboratories 6

By systematically analyzing both time and frequency domain parameters of HRV during polysomnography, particularly during slow-wave sleep, clinicians can obtain valuable information about autonomic function and potential cardiovascular risk.