Blog post by Marco Altini.
In this post I will look at the data more from a user perspective, showing how the iPhone camera data processed by HRV4Training is as good as what you get using a chest strap. In particular, I'll cover the following:
I acquired the data used in this post with a "special app" I made. The app is basically a combination of the Camera HRV and HRV Logger apps, and lets me connect to a Bluetooth SMART sensor and simultaneously collect and process PPG data using the iPhone's camera. The complete PPG raw data is also stored by the app. I used a Polar H7 as reference, since it's the most reliable heart rate monitor I've found so far.
I will first show RR intervals for the three conditions I'm comparing (rest, paced breathing and recovery post-exercise) and then look at HR and time and frequency domain HRV features computed on 60 seconds windows.
A note on data collection and synchronization: The two systems (H7 and camera) recorded data simultaneously, however data is not really timestamped. For example when I get data from the H7, RR intervals are appended to the HR sample every second, however RR intervals are not timestamped and I need to reconstruct the absolute time by summing up RR intervals. In case an interval is not detected or there is noise, the absolute timestamps won't be correct and could get out of synch. This issue explains some of the delays seen between the two data streams. When computing features, these delays are a minor problem, since we compute features using bigger windows (e.g. 60 seconds), and therefore attenuate differences due to the fact that a couple of RR intervals might end up in different windows.
RR intervals - comparison
RR intervals comparison - condition 1: rest
Here are three minutes of RR intervals recorded at rest (sitting):
The time series and histograms look very similar, pointing out already that PPG data can provide high accuracy in beat to beat measurements. Let's have a look at the other two conditions, paced breathing - showing big swings in heart rate due to respiratory sinus arrhythmia - and recovery - showing lower values and variability - due to lower parasympathetic activity.
RR intervals comparison - condition 2: paced breathing
Here are 90 seconds of paced breathing data, as we can see it's deep breaths, about 6 per minute:
Again the PPG time series looks pretty good, following closely the pattern we see for the Polar H7.
RR intervals comparison - condition 3: recovery
Here we have about 2 minutes of RR intervals following a 20 minutes bike ride:
RR intervals are much shorter than in the other two plots (i.e. higher HR), and variability is also reduced (see histogram).
RR intervals seem promising, the time series look very similar. Let's now have a look at HR and HRV features to see if we can extract the same information for both the iPhone's camera and Polar H7. As I discussed in other posts, we are mainly interested in measuring rMSSD and HF, as they are good indicators of parasympathetic activity. Another interesting marker is LF, if we are interested in for example monitoring paced breathing/meditation. In this context we can do breathing exercises (i.e. deep breaths), at relatively low frequencies, that we can capture by analyzing LF. For example, breathing at 6 breaths per minute should result in a peak around 0.1 Hz, which is reflected in the LF feature since LF represent the frequency power in the 0.04 - 0.15 Hz band. Let's start the analysis by looking at HR.
Average HR should be easy to measure using optical methods. IPhone apps detecting HR have been out forever and wrist based sensors showing poor performance for HRV can still measure HR pretty accurately. Our comparison shows what we expect, HR computed using the camera matches very closely what we get with the Polar H7, for all three conditions:
Heart rate comparison - condition 1: rest
Heart rate comparison - condition 2: paced breathing
Heart rate comparison - condition 3: recovery
All HR plots were computed using features collected over 30 seconds and a sliding window of 5 seconds, so over a rather short timeframe. Next, we'll finally look into HRV features.
Heart rate variability features
The following plots show HRV features computed over 60 seconds windows. I plotted features derived using the iPhone camera side by side with features extracted using RR intervals collected with the Polar H7. The data is the same that was used for the plots above. I computed both time and frequency domain features according to the formulas I introduced in a previous post. The sliding window here is 15 seconds, and as usual we look at all three condition separately (rest, paced breathing and recovery).
HRV features comparison - condition 1: rest
HRV features comparison - condition 2: paced breathing
At this point we can start looking at a few interesting aspects. For example, as I introduced before, paced breathing at low frequency rates (e.g. 6 breaths/minute), are reflected in higher frequency power for the LF feature, which includes the 0.1Hz peak resulting from breathing at 6 breaths per minute (LF is defined as 0.04 to 0.15Hz). I'll show comparisons between conditions later on, but you can scroll up and see easily how the LF values are much higher in the paced breathing condition. Also, both conditions are actually recordings taken at rest, which might explain similar values in rMSSD and HF. The situation gets quite different for the recovery recording, shown next.
HRV features comparison - condition 3: Recovery
As expected, for the recovery conditions HRV is highly reduced, with pNN50 going down to zero (i.e. no consecutive RR intervals differ for more than 50 ms), and rMMSD, LF and HF also being much lower. Both the PPG from the iPhone camera and the Polar H7 show the same behavior across time and frequency domain features.
Now let's finally look at a comparison between conditions:
Just looking at the data, we can see that PPG data can discriminate between different conditions as good as the Polar H7 can, for both time and frequency domain features.
We can also look at the HRV features values computed using the iPhone camera and Polar H7 in a different way, plotted against each other for each time window, and all conditions together (plus residuals):
For the ones that believe in p-values, no statistically significant differences are found for any of the features, if we consider a standard alpha value of 0.05 (t-test for AVNN: p=0.56, SDNN: p=0.92, rMSSD: p=0.56, pNN50: p=0.55, HF: p=0.98, LF: p=0.63).
Summary and tips for the camera
I hope with this post I convinced you that the camera version of HRV4Training is as good as the Polar H7. I went through all time and frequency domain HRV features (+RR intervals and HR computed on shorter time windows), for three different conditions in which the sympathetic and parasympathetic systems interact differently. With both the camera and Polar H7 we could easily spot the differences in HRV features that we expected and for all conditions we couldn't find any statistically significant difference between the two sensor modalities.
For my recordings, I stopped using the H7 a long time ago, and went for the convenience of using the camera and not having to wear the sensor early in the morning. However, I realize there are trade offs and it might take a couple of measurements with the camera before you get familiar with it. Here are a few tips to speed up this process:
Since version 4.9.2 HRV4Training provides more guidance on how to place your finger as well as a view of the camera to make sure you properly cover it (see screenshots above).
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This blog is curated by
Marco Altini, founder of HRV4Training
The Ultimate Guide to HRV
1: Measurement setup
2: Interpreting your data
3: Case studies and practical examples
1. Intro to HRV
2. How to use HRV, the basics
3. HRV guided training
4. HRV and training load
5. HRV, strength & power
6. Overview in HRV4Training Pro
7. HRV in team sports
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. Normal values and historical data
7. HRV features
1a. Acute Changes in HRV
1b. Acute Changes in HRV (population level)
1c. Acute Changes in HRV & measurement consistency
1d. Acute Changes in HRV in endurance and power sports
2a. Interpreting HRV Trends
2b. HRV Baseline Trends & CV
3. Tags & Correlations
4. Ectopic beats & motion artifacts
5. HRV4Training Insights
6. HRV4Training & Sports Science
7. HRV & fitness / training load
8. HRV & performance
9. VO2max models
10. Repeated HRV measurements
11. VO2max and performance
12. HR, HRV and performance
13. Training intensity & performance
14. Publication: VO2max & running performance
15. Estimating running performance
16. Coefficient of Variation
17. More on CV and the big picture
18. Case study marathon training
19. Case study injury and lifestyle stress
20. HRV and menstrual cycle
21. Cardiac decoupling
22. FTP, lactate threshold, half and full marathon time estimates
23. Training Monotony
Camera & Sensors
1. ECG vs Polar & Mio Alpha
2a. Camera vs Polar
2b. Camera vs Polar iOS10
2c. iPhone 7+ vs Polar
2d. Comparison of PPG sensors
3. Camera measurement guidelines
4. Validation paper
5. Android camera vs Chest strap
6. Scosche Rhythm24
7. Apple Watch
9. Samsung Galaxy
1. Features and Recovery Points
2. Daily advice
3. HRV4Training insights
4. Sleep tracking
5. Training load analysis
6a. Integration with Strava
6b. Integration with TrainingPeaks
6c. Integration with SportTracks
6d. Integration with Genetrainer
6e. Integration with Apple Health
6f. Integration with Todays Plan
7. Acute HRV changes by sport
8. Remote tags in HRV4T Coach
9. VO2max Estimation
10. Acute stressors analysis
11. Training Polarization
12. Lactate Threshold Estimation
13. Functional Threshold Power(FTP) Estimation for cyclists
14. Aerobic Endurance analysis
15. Intervals Analysis
16. Training Planning
17. Integration with Oura
18. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV normalization by HR
3. HRV 101