Heart Rate Variability Explained PDF

Hello and welcome back. In this session we're going to talk about what is so magical about heart rate variability. In this deeper dive on heart rate variability, I want to describe what is known as respiratory sinus arrhythmia. This has been something that's been known for a long time, but it's been...

Hello and welcome back. In this session we're going to talk about what is so magical about heart rate variability. In this deeper dive on heart rate variability, I want to describe what is known as respiratory sinus arrhythmia. This has been something that's been known for a long time, but it's been incorporated into devices' measurement of heart rate variability. Effectively, the physiology is that when you breathe in, there are changes within the chest pressure that will cause an increase in heart rate, and when you exhale, you will have a decrease in heart rate. These changes relate to the autonomic nervous system's role in influencing heart rate. This is a figure that helps to capture this idea. On the top graph, we're seeing the heartbeat changes from a standpoint of the beats per minute, and on the bottom graph, we're seeing something that's providing us a measure of breathing rate. It's actually a measure of the carbon dioxide. What this is helping to depict is that with each breath in, we have a increase in the heart rate, and with each breath out, heart rate begins to fall off. These are pretty modest changes, but they are enough to provide us this measure of heart rate variability. Heart rate variability is a measure of the variability in the time between individual heartbeats, and it's typically measured in milliseconds. On the figure that we see here, we're seeing individual classic QRS complexes that we would have from an electrocardiogram. What's being described here is that we can measure the interval between these beats. You might think that there is no difference between these beats as you see them on this slide. But in fact, if we were to measure the number of milliseconds, we'll find that there are small differences between each one. This is referred to as the R-R interval. If you don't know about ECG, this is a QRS complex that we're looking at here. By measuring the time between these are peaks that can tell us what the actual time frame is between each heartbeat. To make the calculation of heart rate variability, all we're really doing is performing some statistics on the difference between those heartbeats. There are many different methods for calculating heart rate variability. One of the easiest to appreciate is what's referred to as the SDRR. The RR here is referring to the RR intervals that we spoke about a moment ago. This is simply the standard deviation of the R-R intervals. This is typically measured in milliseconds. Another measure that's very classically performed is the root mean square of successive R-R interval differences. In fact, I'm providing a depiction from a bio strap device that I used. This is my own data here. What it showing you is a very simple thing that you'll quickly understand even if you don't know about HRV. This is my heart rate. At the time my heart rate was 57 beats per minute. This particular device also measures pulse oximetry, and so I have a normal value there. But then over here it's providing us measures of my heart rate variability. It's providing two different methods. What they're describing here is the RMSSD, which is the one we just spoke about with the root mean square of successive RR interval differences. As highlighted here, on the bottom left, this is a PPG signal, so this is a risk-based signal and it's measuring pressure changes. This is technically not R-R intervals and we'll talk more about PPG versus ECG signals shortly. But what we're seeing is a resting collection for five minutes of my PPG signal. It's from this signal that we're able to get a measure of my HRV. The things next to the actual plot is just a listing of what my average heart rate was during that five minutes, in this case, 57 beats per minute. The device actually has a pulse oximeter, so it was suggesting that it was at 98 percent. Then the last two measures are different measures of HRV. The one we've spoken about, the RMSSD, which in this case was 30 milliseconds. The last one is a slightly different plot, and it's measuring the low frequency to high-frequency variability. What we can expect is that, at an intense day of training might reduce the resting heart rate variability for up to three days. This is the way that it's often being used in training and recovery, is to determine whether one is maintaining a high heart rate variability, which is suggestive of recovery, or they seem to be in a high-stress state. Let's talk about some of the pros and cons of heart rate variability. On the pro side, it's an interesting evaluation of the contributions of the sympathetic and parasympathetic systems. This has been around for a long time this idea of the sympathovagal balance. The reason that, that variability exists is because there is a contribution to our heart rate coming from the sympathetic system as well as the parasympathetic system. This heart rate variability being high suggests that there is a strong influence of the parasympathetic influence. Another pro is that it's a non-invasive measure and that it can accurately be measured during rest. On the con side, it's important to have a clean signal. The devices that measure HRV are quite finicky about what they will consider a clean signal, and you may have to collect several times if there's much motion detected. The other problem is that it's less relevant for capturing training-related to stress. In the moment this is not going to be a measure of the training related stress. Heart rate would still be a more appropriate measure during exercise. Heart rate variability and recoveries where the main action is. It's generally used to determine the athlete's stress at a time of recovery. During a resting period, it's giving some idea about what the recent stress has been for that athlete. It's expected, as we were describing, that higher parasympathetic output at rest will increase the heart rate variability, and this would indicate good recovery. On the other hand, it's expected that lower parasympathetic output at rest will decrease the heart rate variability and indicate poor recovery. There are definitely exceptions to this, and so one needs to be careful to appreciate some of the subtle issues that happen with heart rate variability. This relationship between heart rate variability and stress is important to consider a little further. What we expect is that the heart rate variability response is different depending on the type of population being studied. In unfit or recreational athletes, it's more likely that they experience a considerable drop in heart rate variability falling and increasing in training stress. Whereas the fit and highly trained athletes are more resilient to these changes in heart rate variability when presented with increases in training stress. In these highly trained athletes, it's still can be used to help predict whether they're recovering well or they need more time between important workouts. Heart rate variabilities are very commonly used measure in metrics being used in variables today. It is a measure that can be recorded and followed through training periods, as well as competition periods. However, HRV is likely interpreted in an oversimplified way. Next up we'll discuss which internal measures seem best for characterizing training.