Friday, September 22, 2023

Heart Rate Variability


I have been following heart rate variability (HRV) on my watch and three different apps for the past several years. HRV is defined as the slight variations between R waves in the standard ECG recording.  I have included an example below, illustrating the R-R’ intervals (or RRI) and how they might vary over time.

Since HRV became widely available as a measurement off a watch that is commonly worn by millions of people, the research on this measurement and the variable studied has increased significantly.  For my purposes – HRV is thought to be an indicator of heart health and conditioning and possibly a marker of overtraining – but advice about that varies significantly. Some studies have shown that decreased HRV is associated with an increased risk of arrhythmias.  My recent cardiac ablation and cardioversion seemed to present an ideal situation for further study. 

Before getting into those details the physiology of HRV needs to be considered. The dominant heart rhythm of a normal heart is determined by the sinoatrial (SA) node. This node contains a population of spontaneously depolarizing cells that determine the rhythm and rate of the heartbeat. In addition to the neurophysiology of that cell population several additional factors affect both the rate and HRV.  Primary among them is autonomic innervation from both the sympathetic and parasympathetic systems and their effect at the SA node. Parasympathetic fibers from the vagus nerve modulate slower firing through the neurotransmitter acetylcholine (ACh). Sympathetic fibers increase the rate of firing through the neurotransmitter norepinephrine (NE).  NE has a longer half-life than ACh, but vagal tone is thought to be the most significant determinant of HRV.  That is in line with several clinical observations including lower baseline heart rates in conditioned athletes and higher heart rates in people with less conditioning or in stressful situations.

What happened to my heart rate and HRV during the recent cardiac ablation for atrial fibrillation and subsequent cardioversion?  To answer that question, I had to figure out how to get the data off my Apple Watch 5.0.  The only approach I could find was to downloaded all of the collected Health App data as a CSV file and then plot it in Excel.  There are some online sites that you can download the data to and then use the remote software for plotting, but I preferred to retain control over the data. If you decide to do that and have several years of data like I did – it takes a long time.  It took about 5 hours in my case to download about 1G of data to a zip file.  From there it is easy to open that file with Excel or other software and do the plots. A useful addition to the Health App would be able to download specific time intervals.

I have done 2 plots so far based on average daily HRV and hourly HRV as shown below.

 The plots are interesting because it clearly shows an effect from the ablation, a 96-hour period of atrial fibrillation and atrial flutter, and the cardioversion. At the minimum the baseline HRV drops to a different baseline after the ablation. That is followed by a significant spike with the recurrence of afib/flutter.  And then there is a return to the lower baseline after the electrical cardioversion.  I rarely had any significant episodes over the course of a year and whenever I went back and reviewed HRV it was not significantly changed. Since all those episodes were typically less than 2 or 3 hours it may not have been long enough to see an HRV effect.  Conversely spikes of 50-100 msec in the HRV recording were common and not associated with arrhythmias. In the case of the post ablation period the sustained rates were associated with spikes, but since atrial flutter is regular, the associated R-R’ intervals probably showed a more characteristic HRV.

I would expect to see an increase in vagal tone and therefore HRV just related to the sustained high rates over 4 days. If increased vagal tone correlates with increased HRV that does not seem to be the case in these graphs. The graphs also seem to indicate to me that there may be a structural element to HRV – either in the anatomical configuration of the conducting cells, their altered physiology, or a combination.

The main implication for me at this point is to cautiously restart my conditioning efforts and see what impact that has on the HRV baseline.  A second question is whether my HRV will approach the pre-ablation baseline.  Electrocardiograms (ECG) may provide some clues in that direction.  I have listed them below for references. Significant changes occurred in the immediate post ablation ECG and the post cardioversion ECG.

An additional thought is whether non linear analysis of the RR intervals would yield more information and easily interpretable graphics. I have used some of these attractor plots in the past and also applied them to single electrode analyses of normal controls and patients with Alzheimer's disease. In terms of ECG analysis - see figure 5 in reference 2. In terms of theory - these attractor diagrams also imply changes in biological complexity at either the structural or functional level - see the diagrams at the bottom of this post


George Dawson, MD, DFAPA

ECG time course (1 -> 5 are in sequence):

1.  Baseline - preop ECG 

2.  Post ablation ECG (following day):

3. Post ablation ECG - note anterior T wave changes thought to be consistent with procedure.

4.  Precardioversion ECG showing atrial flutter at a high rate (day 5 of this arrhythmia; post op day 14).

5. Post cardioversion ECG showing NSR but flipped T waves in V1-V3.

6. ECG follow up 2 weeks after cardioversion showing T wave normalization in anterior leads.

Heart Rate Variability

Some recent recovery in HRV after a long period of low numbers in the 7-37 msec range following ablation and cardioversion.



1:  Fojt O, Holcik J. Applying nonlinear dynamics to ECG signal processing. Two approaches to describing ECG and HRV signals. IEEE Eng Med Biol Mag. 1998 Mar-Apr;17(2):96-101. doi: 10.1109/51.664037. PMID: 9548087.

2:  Nayak SK, Bit A, Dey A, Mohapatra B, Pal K. A Review on the Nonlinear Dynamical System Analysis of Electrocardiogram Signal. J Healthc Eng. 2018 May 2;2018:6920420. doi: 10.1155/2018/6920420. PMID: 29854361; PMCID: PMC5954865.

3:  Aston PJ, Christie MI, Huang YH, Nandi M. Beyond HRV: attractor reconstruction using the entire cardiovascular waveform data for novel feature extraction. Physiol Meas. 2018 Mar 1;39(2):024001. doi: 10.1088/1361-6579/aaa93d. PMID: 29350622; PMCID: PMC5831644.

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