by Markus Rummel. We present the first results of AI Endurance's new capability to calculate Respiration Frequency (RF) from in-activity heart rate variability (HRV) data. RF demonstrates its potential in assessing the validity of HRV threshold determination.
How can we calculate RF from HRV data, specifically the RR interval time series? RR intervals are the precise time between each successive heart beat.
Respiration Frequency can be determined from RR intervals because of the natural influence of breathing on HRV, particularly through a phenomenon known as respiratory sinus arrhythmia [1]. During inhalation, the heart rate typically increases, and it decreases during exhalation, creating rhythmic fluctuations in the RR intervals—the intervals between consecutive heartbeats. These variations arise due to the autonomic nervous system’s response to breathing patterns, modulating the heart rate in sync with the respiratory cycle.
By analyzing these RR interval fluctuations, it becomes possible to estimate the frequency of respiration, even outside of controlled environments. This approach provides insights into respiratory patterns and reveals the coupling between cardiovascular and respiratory systems, offering valuable information for monitoring physiological states and stress levels in both daily life and athletic performance contexts.
There's a few options to measure your Respiration Frequency more direct although these options are less affordable than simply wearing a heart rate monitor (HRM) chest strap that measures high quality HRV data such as the Polar H10:
For a blog post comparing alphaHRV RF calculated from HRV to Hexoskin 'directly' measured RF, see this blog post by Bruce Rogers. While awaiting more in-depth studies of comparisons of this nature, the results are very encouraging in that the alphaHRV RF algorithm is clearly doing an excellent job in estimating RF. Also alphaHRV has actually improved the RF algorithm since the above evaluation.
AI Endurance is now using the same RF algorithm as developed by alphaHRV. As expected, the RF calculation in AI Endurance compares very closely to that of alphaHRV:
Fig 1: alphaHRV vs AI Endurance RF calculation for an easy ride with some intensity at the end. The slight shift to the right of the AIE green curve is due to alphaHRV calculating the data in real time while AIE calculates after the activity has been completed. AIE centers the interval of HRV data to calculate RF around a given timestamp while alphaHRV does this at the 'right edge' with all HRV data in the past of the current timestamp due to the real time constraint.
So how can we make use of this new metric Respiration Frequency to improve HRV threshold [2, 3] detection? An obvious choice seems sanity checks: if we are detecting thresholds with increasing power/pace in the order
We would also expect respiration rate to increase at each threshold. If this is not the case we can discard thresholds.
Fig 2: baseline (a1 > 1), aerobic (a1=0.75) and anaerobic (a1=0.5) power and RF thresholds.
This is an example of cluster threshold detection in every day activities over a 2 week time span. Cluster thresholds simply look at the distribution of power/pace around the characteristic a1 values (>1 for baseline, 0.75 for aerobic, 0.5 for anaerobic) for each workout before significant fatigue is expected to kick in. The threshold is the average pace/power of each distribution.
In this example, while as far as power is concerned there's a monotonic increase in all detected thresholds from baseline to 0.75 to 0.5, this is not the case in the mirror plot for RF. We would discard the green, light blue and blue-gray bottom three data sequences because the RF at 0.5 is actually lower than the RF at 0.75. We would only keep the yellow and purple threshold detection.
Ideally, we would be able to detect the aerobic and anaerobic threshold independently of a1, purely within the Respiration Frequency metric. Combining a1 and RF threshold estimation can lead to improved accuracy and reduced bias [4, 5]. The procedure is not necessarily easy to automate though as we will discuss.
A common method is to look for breakpoints in breathing patterns at the gas-exchange thresholds [6]. This is usually done as an analysis of time vs RF in a ramp protocol. Breakpoints are identified after fitting a 6th degree polynomial and identifying local maxima of the second derivative of the latter as the breakpoints/thresholds.
We tried to take this method one step further by looking at intensity in terms of power/pace or heart rate vs RF: For a given workout, we bin each data point by heart rate and calculate RF as the average of all data points in the bin.
Fig 3: Binned heart rate vs average RF in each bin with 6th degree polynomial fit.
As expected, we get a steadily increasing average RF with increasing intensity. In this example with heart rate vs average RF. We can do the polynomial fit and look for local maxima in the second derivative, just like in [6].
Fig 4: Second derivative of the 6th degree polynomial fit in Fig 3 with local maximum.
However, we found that we can not reliably detect local maxima in an automated way over our dataset, as demonstrated by the example in the above figure.
Other attempts include change point detection in various metrics: RF, first derivative of RF, RF/a1, first derivative of RF/a1. Again, these did not yield a reliable threshold detection method.
While we continue to work towards improved threshold detection with the help of Respiration Frequency data, we are grateful for any input or ideas from the community. Feel free to reach out at info@aiendurance.com.
The world around us is changing, driven by technological advances that seek to improve our lives.
DFA (detrended fluctuation analysis) alpha 1 is an HRV (heart rate variability) based aerobic and anaerobic threshold estimation method. It only requires a heart rate monitor that tracks HRV data. It has the potential to track your thresholds automatically without dedicated test workouts.
by Grant Paling. In his second of three blog posts, Grant shares his personal experiences with AI in endurance training, emphasizing the blend of AI assistance and human accountability in achieving his personal triathlon goals.
When it comes to excelling in endurance sports such as triathlon, running or cycling proper nutrition plays a crucial role in maximizing your performance and achieving your goals. Whether you're swimming, cycling, or running, your body requires optimal fuel to meet the energy demands of these activities. In this blog post, we'll explore the importance of nutrition in triathlon, running, and cycling, followed by an introduction to the new feature of AI Endurance which provides recommendations for daily and activity specific nutrition requirements individualized for each athlete.