Heart Rate Variability – Improving Health Indicators

Heart Rate Variability – Improving Health Indicators

The last decade saw a growth of wearable devices that can measure and keep vast stores of information about ourselves and the surrounding environment. From our own smartphones to watches, bands, glasses and even clothes, the term “smart” now applies to many of our day-to-day objects. The “quantified self” culture has grown exponentially: from 84 million wearable devices in 2015 to a staggering 527 million in 2020.

Recent advances in the field of sleep studies have allowed smartwatches to measure the quantity and quality of our sleep cycle. While offering valuable insights, these measurements are not entirely accurate and cannot be compared to clinically-validated studies, performed using a method called polysomnography which extracts multiple signals. One of such signals is the ECG, or electrocardiogram, which measures the heart’s rhythm using electrodes to measure the electrical activity.

The advancements that led to the availability of these consumer-grade devices were based on ECG signals and on a physiological phenomenon called Heart Rate Variability, or HRV. This corresponds to the changes in the time intervals between two consecutive heartbeats, hence the term variability.

One of the main factors that trigger these fluctuations is the activity from the sympathetic and parasympathetic divisions of the autonomic nervous system (ANS), which has distinct and contrary effects both on the heart rate (HR) and heart rate variability (HRV). There are other factors that could be involved, such as cardiorespiratory feedback, but its effect is still contested.

How does this relate to the sleep cycle?

A normal sleep cycle is composed of 4 stages, Wake, Light, Deep, and REM Sleep. This cycle repeats on average 4-6 times, each lasting around 90-100 minutes. The following are some of the key physiological differences between each sleep stage:

  1. In light sleep (Non-REM Stages 1 and 2), our muscles begin to relax, our heartbeat slows down, and our temperature drops.
  2. In deep sleep (Non-REM Stages 3 and 4), the body enters in “recovery mode”, promoting cell regeneration, tissue growth, among others. At this stage, the heart rate is much more regular.
  3. In REM sleep, the sleep phase where 80% of our dreams occur, we spend as much or even more energy than as if we were awake. Heart rate and breathing are faster and more irregular.

Since the beginning of the millennium, more studies found correlations between HRV and distinct stages of sleep. By performing an analysis not only on some HRV signal features but also by means of signal transformation, one could see there were specific frequency patterns in different sleep stages:

With the advancement of machine learning and computational power, HRV signals could be used to train an algorithm that classifies sleep stages according to the distinctive features between each individual sleep stage. It should be noted at this stage that the performance of a machine learning classifier depends on the quality and coherence of the dataset provided.

Many of the scientific publications that illustrate studies on sleep stage classification using HRV present one significant conclusion: individual physiology can differ greatly with respect to sympathetic/parasympathetic balance and sleep stages. Therefore, it would be an oversimplification to consider only a single factor to classify for sleep stages when there are a plethora of factors that influence our heart rate variability: age, gender, weight, hormonal factors, stress, medications, to name a few. As such, most studies point to a general relative accuracy in sleep staging between 70 and 90%.

Despite its shortcomings, the capacity to measure sleep stages on a wearable device should not be overlooked. In 2014 in the USA alone, approximately 64 million people suffer from a moderate or severe form of obstructive sleep apnoea, while less than 1 million sleep studies were performed, the key conclusion being the economic cost of performing such a study. Wearables are now also able to infer the likelihood of an apnoea event, which would assist in the diagnosis process.

Other Uses for HRV Analysis

Besides sleep apnoea, HRV analysis has other uses that are now gaining more attention. An abnormal value of HRV is associated with, for example:

  • Susceptibility to sudden infant death syndrome (SIDS)
  • Post-traumatic stress disorder (PTSD)
  • Lack of emotion regulation and decision-making
  • Susceptibility to depression and anxiety

Exercise and Breathing

One critical point to take note of is that heart rate variability can be “trained”, that is, one’s fitness level can be measured by HRV analysis. An athlete normally exhibits better cardiac responsiveness to varying conditions. The heart’s ability to vary its rate is indicative of its adaptability to different stimuli – being ready to perform in a short amount of time requires a well-prepared control mechanism of the cardiovascular system.

One final word should go to cardiorespiratory feedback. The below picture represents a simplified mechanism of correlation between respiration and ECG signals. There are specific breathing patterns (ranging from 4.5 to 6.5 breaths per minute) that stimulate the baroreceptors, part of the blood pressure control mechanism. This, in turn, influences heart rate:

This suggests that breathing modulates the heart rate and blood pressure. However, the relationship is not clear, as more studies are required: some hypotheses point to synchronization between the heartbeat and the respiratory rhythm, others question whether this mechanism is an intrinsic function of the cardiopulmonary system.


Heart Rate Variability has gained increased attention since the 1970s. The correlations between the autonomic nervous system and the heart rate variability have opened the door for clinical applications as well as more research opportunities. With the advent of wearable technology and the application of machine learning algorithms, the interactions between heart and brain are being further explored. These will become a larger trend in the next years, fuelling our interest in knowing ourselves better.


[Figure 1] Goldberger, A., Amaral, L., Glass, L., Hausdorff, J., Ivanov, P.C., Mark, R., Mietus, J.E., Moody, G.B., Peng, C.K. and Stanley, H.E., 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation [Online]. 101 (23), pp. e215–e220.

[Figure 2] I, RazerM, CC BY-SA 3, via Wikimedia Commons

[Figure 3] B.V. Vaughn, S.R. Quint, J.A. Messenheimer, K.R. Robertson. Heart period variability in sleep. Electroencephalography and Clinical Neurophysiology, Volume 94, Issue 3, 1995, Pages 155-162.

[Figure 4] Morgan, E., All About HRV Part 4: Respiratory Sinus Arrhythmia, viewed 20 October 2021.

About the Author

Luís Mendes - Technology Advisory

Luís is a techno-functional SAP specialist, who is passionate about technology and innovation. He is skilled in problem-solving and business transformations, which enables him to deliver across various industries.

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