The Ultimate Guide to Heart Rate Variability (HRV): Part 1

Measurement setup, best practices, and metrics.

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Morning measurement using HRV4Training, the only validated camera-based app

Part 1: measurement setup and first recordings

But first, Just a tiny bit of theory

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Example of a few seconds of ECG data, including detected beats. The time differences between beats are called RR intervals and are the basic unit of information used to compute HRV. We need several RR intervals to be able to compute your HRV. This is why HRV needs to be computed over a certain amount of time, typically between 1 and 5 minutes.

How do you measure?

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In this figure, we can see one minute of ECG data (similarly to the data we saw before, but a bit more compressed as we have 60 seconds instead of just 6, shown in lighter gray). We can also see one minute of PPG data, in black. PPG data here was collected using the phone camera. We can also see detected ECG peaks (used to compute RR intervals), and detected PPG peaks, perfectly aligning over the minute of data. This plot should make it easy to understand that both methods are equivalent, and HRV data can be acquired accurately using optical devices. Note that not all optical sensors are able to do so, hence it is important to make sure the device you use has been validated.

What’s the difference then?

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Physiological stress comes from different sources, all having an impact on our ability to deal with additional stress and therefore of maintaining or improving our health and performance. Here we can see how both running a marathon and a few days out of the ordinary for new years trigger a baseline change below normal values, a clear sign of high stress and difficulty to cope. More on this in the next parts. Data here was collected daily using morning measurements and HRV4Training.

Remember, consistency is key

What does the HRV number tell me?

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Most HRV metrics you will see in apps and other systems (for example rMSSD, recovery points, or SDNN) can be interpreted as shown in this figure, meaning that a higher score is typically associated with higher parasympathetic activity and more rested state. On the other hand, a lower value is associated with higher stress. Note that higher or lower makes sense only when compared to your historical data, and accounting for normal day to day variability, all topics that I will cover in Part 2 of this series. This is an oversimplification, but nonetheless a good starting point.

What’s next?

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Written by

Founder HRV4Training.com, Data Science @ouraring Lecturer @VUamsterdam. PhD in Machine Learning, 2x MSc: Sport Science, Computer Science Engineering. Runner

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