Monday, October 24, 2022

alphaHRV vs Garmin resp rate - David vs Goliath

Continuing with the theme of respiratory rate tracking, this post will explore the brand new bonus feature of alpha HRV that tracks this metric.  Since Garmin has their own methodology in doing this, we will be looking at how they do side by side.  This will be a very interesting comparison for several reasons - 

  • They are deriving resp rate from identical RR intervals (in this case the Polar H10) 
  • Use the same recording device (Garmin Epix 2 in my case)
  • The Garmin implementation is backed by their Firstbeat division, a professional group of engineers with a substantial budget and resources.
  • Alpha HRV is written by two young programmers who wrote the respiratory rate code in about 2 weeks in their spare time.

So truly, a David vs Goliath contest. 

The testing details: The subject (me) wore the Hexoskin vest as the "gold standard" resp rate device.  This measures resp rate through chest/abdominal wall motion sensors and is quite accurate.  I also used the Movesense Medical ECG given it's great track record for ECG derived resp rate.  Lastly, the Polar H10 was worn along with the Movesense belt, recorded to the Garmin Epix 2 watch.  The watch was running alphaHRV in the cycling activity mode.  This way, both alphaHRV and the Garmin algorithms would do their own computation of resp rate based on RR intervals from the same device.

The test consisted of a brief warm up then a cycling ramp to near failure (15w/min rise using Zwift) followed immediately by 90 min in zone 1.  In this way we have a handle on ramp resp rate behavior for those who are interested in using this for zone demarcation purposes as well as a steady state long interval to demonstrate how the apps handle stable intensities.  AlphaHRV was set to a smoothing of soft and a window of 45 seconds.  Kubios was set to a window of 30 seconds:


As one can see from the above, the post ramp HR (blue) was remarkably steady for the next 90 min, no real sign of drift.

Post ramp results:

Garmin (using H10 based RRs via bluetooth, artifact<1%):

  • In all the tests, the black line will be the Hexoskin displayed as a 30s moving average.  The red is HR, Green is Garmin resp rate.
  • The Garmin substantially underestimates resp rate, but is stable throughout.

AlphaHRV:


  •  Much better than Garmin, with alphaHRV (light blue) following the pattern nicely.

Kubios using RRs from the H10 (just like Garmin and alphaHRV does):

  • For the most part, good tracking with a couple of areas of "blips" where correlation is poor.

Kubios + Movesense ECG (using ECG voltage and RRs):

  • The Movesense (yellow) very closely follows the Hexoskin and continues to show why this combo is yields great ECG derived resp rate results.
 

The Ramp (Hexoskin in black):

Garmin (green):

  • Both the absolute numbers and shape (drop near the end) do not match the Hexoskin.

Alpha HRV:

  • Both the general shape and absolute values are closer to the Hexoskin than Garmin, but there are some blips along the way.
  • These "blips" will degrade the correlations and threshold curves.

Kubios using H10 RRs:

  • A good showing here for Kubios, with a reasonable shape and overall behavior.
  • There was a bit of a drop in the middle that could mess up threshold determinations.

Kubios + Movesense ECG:

  • Movesense ECG with Kubios matches the Hexoskin very well. 
  • Excellent shape and correlation.

Some comparison stats:

Correlation - here we plot Hexoskin on the X axis and each challenger on the Y.  The higher the R squared, the better the fit/matching.

Garmin:


AlphaHRV:


Kubios RR intervals:


Kubios + Movesense ECG:


Interesting observations:

  • The Movesense ECG + Kubios has a superb correlation coefficient (.81).
  • Both AlphaHRV and Garmin have moderate correlations.
  • Kubios RR derived resp rate is midway between the ECG and AlphaHRV.
  • This is an great example why a simple Pearson correlation coefficient or equivalent is not sufficient for comparison studies - you can have good correlation but a substantial deviation in the difference in values (aka, the Bland Altman analysis).  If we do a Bland Altman plot, the Garmin would have had a huge bias:
The Garmin data is shifted down about 12 breaths/min with very wide scatter - both facts attesting to it's poor agreement tot he Hexoskin.
 

Thus far we can see that the alphaHRV app does do a much better job of tracking resp rate than Garmin, but it's not up to the precision of Kubios RR methodology nor the ECG derived resp rate.

What about ventilatory thresholds?

The last type of analysis I would like to look at is how the ramp values looks when plotted as a 6th order polynomial.  Why?  A key study done by Cross et al. uses a 6th order polynomial to help calculate the first and second ventilatory thresholds using resp rate data.  Here is a figure from the study with a red arrow pointing to the 6th order curve:

  • The idea is to figure where the slope maximizes at the 2 threshold points.  See the citation for details.

How do our various recording methods stack up with 6th order plotting?  I'm not going to look at the Garmin since the values have such an enormous bias.

Hexoskin (gold standard):



AlphaHRV:

Kubios RR:

  • Unfortunately, both the AlphaHRV and Kubios RR curve shapes do not conform to the Hexoskin shape.

What about the Kubios + Movesense ECG?


  • Wow - pretty close to the Hexoskin!
  • I continue to be impressed with this combo of equipment and software.
  • If one were to try to derive VT1 or 2 from resp rate data, the Movesense ECG + Kubios would be the way to go.  With the other methods YMMV.

Some conclusions:

  • Garmin's resp rate algorithm is not very accurate.  The bias is very high and correlation modest despite high quality RR recordings.
  • AlphaHRV tracks resp rate well, especially at steady state conditions.  It potentially can do well during a ramp (from what I've seen of other examples) but in the above case, it was not good enough for threshold estimation based on published models.
  • We again see that the combo of the Movesense ECG with Kubios software is both able to track resp rate at steady state conditions and do very well during incremental ramps.  It can also reproduce the 6th order polynomial plot for threshold estimation.  However, there is a cost associated with this combo that is not trivial.
  • One wonders, what is going on with the Garmin/Firstbeat division.  These programmers/scientists are supposed to be top notch, have years of development under their belt, large financial and testing resources.  How can they release such a flawed product?  It also makes one not trust their other metrics and claims (training readiness, body battery, sleep scores etc).  Although it's impressive that a couple of young programmers working in their spare time have easily surpassed them, I think it's shameful that Garmin puts out this type of software quality.  
 
  • Bottom line - David beats Goliath.

 









Thursday, October 13, 2022

Kubios Scientific and DFA a1 thresholds

Acknowledgment - I would like to thank the Kubios team and project founders for continuing to enhance and improve it's functionality.  In addition, I can honestly say that most if not all of the interesting work we have done with DFA a1 and exercise physiology may not have occurred without this program. A good analogy is that of a grand tour rider and their teammates/support staff - it really does take the entire team to create a winner. 

Updated 10/24/22 - added section on multi user/athlete recording and cloud uploading 

Last week a new, enhanced version of Kubios HRV software was released that includes a "training report" and direct Polar H10 ECG recording with cloud file storage.  What this means is that for those of us interested in HRV related intensity thresholds, the software will now do an automatic calculation showing both VO2 and HR at the AeT (VT1) and AnT (VT2).  In addition, using the companion mobile app, you can record Polar H10 ECG (with a running visualization) for later cloud upload and desktop analysis.  These features are on top of the previously discussed respiratory rate estimation based on either HRV or ECG fluctuations.

ECG recording

Before going into the Training Report with DFA thresholds and intensity zone tracking, a brief look at the ECG recording capability is in order.  You will need to be a registered user for the Scientific version to save the ECG file.  As discussed in previous posts, ECG display is an integral part of not only getting the most precision out of HRV recording, but can make a huge medical difference by identifying arrhythmia and early signs of atrial fibrillation that can be common in endurance athletes.  As discussed above, there are smartphone apps to record continuous ECG data from the H10, but many are not completely stable and crash when you need them the most.  I'm hopeful the Kubios implementation will not suffer from that issue.

Open the Mobile app - Start a Custom measurement - Choose the ECG toggle - Record the tracing:

When done, upload it to the Kubios cloud site, then "sync" the files to your PC afterward (the sync function is in the PC program).

Are there advantages to this over some of the other apps?

Yes and No - All apps at their best will record the voltage over time from the H10 unit (all at a fixed 130 Hz sample rate).  However, if you were a coach, running a lab or involved with groups of athletes/patients and you wanted to monitor and store the records, the Kubios solution seems the optimal route. 

 

Training Report

This is where several metrics are tracked through part or the entire exercise session (HR, Resp, DFA a1, VO2 estimate).  This includes DFA a1 threshold determination.  I will admit that I have no idea how it's done, that info is not in the documentation.  So let's just skip to the question of how well their net HR result agrees with the conventional way we calculate the HR at the first and second thresholds.

Before jumping into that, I recommend going to the preference menu and changing the default nonlinear beat options to the one below (short term fluctuations to N1 between 4 and 16):

Note that the N1 default is 4 to 12 which may yield different DFA a1 results

Window width = 120 sec


Make sure Ventilatory thresholds are calculated as DFA a1.

Next - Load a ramp file (File/Open).  As of this writing, I'm not sure how well the algorithm will work with a mixed session (or multiple intervals) for threshold analysis.

Select the Reports menu - Training Report 


You will then get this dialog box:

 

Select "Whole data" and detect Recovery automatically.  Note -There is a bug in the app that may cause a crash if you have a ramp embedded in a long session, and then select just the ramp (using custom start and end time) and then try to do the report (so don't select any intervals).  For now, stick to "ramp only" files.

Training report output:

  • The red arrow points to the HRVT 1/2 (184 and 195 bpm in this case).
  • The red box is the DFA a1 over time (using the default "auto" artifact correction), an estimate of VO2 (using established equations based on age, BMI, HR or user defined).
  • The green box is the HR/respiratory rate over time.

The above report is based on one of the participants in our original validation study - and the numbers were quite close to what I obtained manually (plotting the HR vs DFA a1 using linear regression - bottom figure):

  • Since the Kubios training report HRVTs were so close to the "hand done" output, I decided to go through the entire participant group and compare "manual" vs whatever Kubios Scientific is doing.

Let's take a look:

Regression plots for each threshold:

First (AeT)

Second (AnT)

  • R squared correlation coefficients are very good, if not excellent (more on this below)
  • HRVT2 appears to be tighter than HRVT1.

Bland Altman difference analysis:

HRVT: 


HRVT2:


  • Many results are spot on but some are noticeably different.
  • Mean first and second threshold determinations for the entire group were very close - within 1 bpm:


 

Why they are not identical?

  • Possibilities include the underlying methodology (we used HR vs a1 regression through the near linear section of decline).  The "training report" might use something else (a1 vs time then back solve for time:HR/VO2).
  • The choice of which points to use for regression is another major factor.There is no exact standard - I personally look for the series of data points to optimize a straight line.
  • Does Kubios account for nadir values (and not to plot them) and how early does it start to count the points for regression?  

Another interesting issue (in one of the participants) was failure of the "automatic" artifact correction method to properly handle a few atrial premature complexes resulting in a skewed data series:

On a first look through the participant data, there was an outlier who had this training report.  Outlier meaning the Kubios threshold was very different than the one I derived.

  • As you can see, the DFA a1 over time drops then rises again, then drops (purple arrow and circle).

On closer look, my original analysis did not have that (from my old worksheet):

  • No up/down bump - why?

A look at the ECG tells the story:

Here is the original raw file:

  • There is a series of 3 non consecutive APCs (the green arrow and green circle show the middle APC in the blue 2 minute measurement window.
  • The app does not seem to think these beats are abnormal, keeps them in the series and the result is the bump in a1 (orange circle on bottom pane).

However, if I manually delete just those 3 beats (which I did on the original years ago), the plot smooths out:

  • I unchecked the 3 APCs (no red "+" anymore at the green circle) and the a1 flattens out (yellow below).
  • This is meant as a warning - look at the DFA a1 over time output - if it has a strange pattern, either redo the test, consider other reasons for that behavior and look at artifacts carefully.
  • The HRVT was markedly improved by doing the beat correction.

However, if a coach, physiology lab or group were interested in a quick, objective ramp threshold estimation, this appears to give reasonable results (especially if nothing fishy appears in the a1 tracing plot).  Certainly, YMMV depending on artifact, signal quality and day to day variation. The artifact issue above would affect a manual a1 calculation as well.

Training Zones:

As part of the training report, the app will now calculate the amount of time you spend in zone 1, 2 or 3 as determined by DFA a1.  This is potentially a game changer!  Previously, you would have needed to export the time varying output to Excel and manually do the calculations.  None of the Web apps (Runalyze or AIEndurance) inform you as to the zone times related to DFA a1.

Here is one example of my own reviewed in the previous post as an illustration of lactate/respiratory rate vs cycling power to determine the polarization index (4 minutes in zone 3 by cycling power, but far longer according to lactate/resp rate):

The DFA a1 zone report is boxed in green (listed as "HRV thresholds"):

  • Over 8%(12 min) of the session was spent in zone 3 (a1 < .5) and 75% in zone 1 (a1 > .75).  That 8% (12 min) in zone 3 (by DFA a1) contrasts to the total of 4 minutes spent in zone 3 calculated by power (see the 2 spikes in HR corresponding to the HIT intervals).
  • This again illustrates the difference between measuring exercise load from external markers (power) vs internal markers (DFA a1, resp rate, VO2 or even lactate).
  • One must also realize that fatigue/illness or any other condition that may suppress the autonomic nervous system can alter DFA a1 behavior.  Hence, repeating the exact same workout as above if I was fatigued/over reached would have skewed the zone report. That is still a potentially helpful clue to ANS status and whether or not you should back off on training intensity.

Multi User input:

For those of us managing multiple tests subjects, athletes or rehab clients, I would like to go through the appropriate procedure to manage this.  As an example, let's say you have the Scientific version and have 2 test centers running simultaneously with each tester having a different device (android or ios).  They would both need Kubios mobile on their phones then log in with your user name and password.  Once on the main screen.....

Go to  - a new Custom measurement on the main screen:


Press that and we get this:

  • I have chosen both RR and ECG, but my name is in the "subject" box.
  • We want to change that:

What needs to be done is to press on the "Bruce Rogers" Subject box to get this:

We will need to press the "Add new" on the top right.

Then you can enter the details for the new user, or whoever is getting the test:

I added "New user" but did not fill out the details below it.  Once done, hit the "Done" button

Now we are back to the original screen, but instead of my name, the "new user" is selected.  On the info box below it, you can add any details about the test.

Then just press the proceed button and you can start the recording:

Press start
When finished, save the recording, go to your PC, then to the Kubios main app, select Tools/Kubios cloud sync, chose the synch process, then go to the folder you selected for downloads to view the results.




Summary:

  • The new Kubios HRV Scientific software is now capable of automatic calculation of the HRVT1/2. For accurate readings, perform an incremental ramp when physically and mentally fresh.
  • It appears that the agreement with "manual" methods is excellent based on comparing it to our original studies.
  • However, blind usage of the resultant HR data may be problematic. Make sure there is a section of near linear DFA a1 decline over time - if so, the resultant threshold output will probably be close to doing it by hand.
  • Having ECG output is indispensable.  It's important to both optimize signal strength, waveform shape and perhaps most importantly, differentiate noise artifact vs arrhythmia. If you do see aberrant beats as in the above example, consider manual beat deletion if the DFA a1 curve is atypical.
  • Kubios Scientific is capable of directly recording the H10 ECG with the companion mobile version for iOS or android.  There is cloud storage ability to be used with the PC version for detailed review.
  • As an added and unique feature, training zone distribution based on an autonomic (internal load) marker is automatically calculated using DFA a1 data.
  • Finally, remember to interpret DFA a1 as an indicator of ANS status and as part of the "network" theory of exercise physiology.  Inappropriate suppression compared to prior historic values may be due to many diverse factors (fatigue, illness, de-training).



Heart rate variability during dynamic exercise