deep-EKG - FAQ

DFA might prove to be a groundbreaking advancement to correctly set our training zones and track fitness. It only requires you to wear a heart rate monitor that tracks HRV (Polar H10 recommended). This way, you will be able to

Determine your aerobic and anaerobic thresholds. For example, your aerobic threshold sets the upper bound of your Endurance training zone. It is important this bound is set correctly, to properly polarize your training, and actually take your easy activities easy for constructive training progression.
Avoid pitfalls of common test protocols. If you don't have access to a physiology lab, you can currently assess your aerobic threshold as the pace/power where you can comfortably hold a conversation. Since this method is obviously not very accurate, DFA presents an accurate but affordable and non-invasive alternative. For the anaerobic threshold, time trial fitness tests are hard to pace and lead to significant fatigue. On the other hand, ramp tests which take a percentage of your max power/pace as your anaerobic threshold depend on the ramp speed [3] and often poorly correlate with your threshold.
[3] Establishing the VO2 versus constant-work-rate relationship from ramp-incremental exercise: simple strategies for an unsolved problem, Iannetta D, et al, doi: 10.1152/japplphysiol.00508.2019
Get high quality threshold data into AI Endurance - all the time. Both thresholds provide defining information about your current fitness state. Therefore, assessing them regularly is crucial for our AI to determine which training leads to successful outcomes. For example, AI Endurance ($20/mo subscription) has automated these assessments from your HRV data to the point that you don't even have to do dedicated tests anymore.

Imagine being able to detect your fitness level during most activities that are hard enough to cross either threshold, without even thinking about fitness testing.

https://aiendurance-com.translate.goog/blog/dfa-alpha-1-app-to-track-your-heart-rate-variability-thresholds

How can Deep-EKG App detect my DFA aerobic and anaerobic thresholds?

We detect thresholds via clustering: a cluster threshold is the average of all heart rate / pace / power values recorded during the first 30 minutes of an activity with DFA values close to 0.75 - aerobic threshold - LT1; or 0.50 - anaerobic threshold - LT2.

No more fitness testing anxiety and fatigue - focus on your training plan and we’ll detect your thresholds automatically over time and keep your digital twin up to date.

How should I set my training zones in any Fitness App based on Deep-EKG threshold detection?

The aerobic threshold - LT1 - sets the upper bound of your Endurance training zone.

The anaerobic threshold - LT2 - sets the upper bound of your Threshold zone.

Now there is a way to keep track of your training zones as they evolve over time. As your fitness evolves, DFA data will indicate an update to your zones might be warranted.

What about artifact detection?

Both recovery HRV and DFA threshold detection are very sensitive to HRV artifacts. Artifacts are missed, short, extra, and ectopic beats. Some experts believe artifact correction is crucial for a meaningful analysis of HRV data. On the other hand, at Deep-EKG, we believe attempting to remove artifacts from the R-R interval does as much statistical harm as good!!! In addition, to work with as few artifacts as possible, the Polar H10 is the recommended heart rate monitor of choice.

What is Major Peaks as determined by EKG Fast Fourier Transform?

Instead of removing artifacts, the Deep-EKG App cross checks DFA threshold calculations using a parallel digital signal processing engine, called the fast fourier transform (FFT). Our patented EKG FFT algorithm counts major peaks (MP), which also decrease dramatically in parallel with DFA as exercise thresholds are approached. We only define: aerobic threholds - LT1; and anaerobic thresholds - LT2; when there are DFA and MP clusters, which defines when these exercise thresholds are reached.

https://patents.google.com/patent/US11883177B2

https://patents.google.com/patent/US11331032

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