DFA a1 and ChatGPT interview

We all know of ChatGPT and the wonderful era of "AI" (sarcasm) it heralds. No, this will not be some opinion piece about that, but a more focused look at how well the app does with DFA a1 related questions. Because on a practical level, folks are not always going to find what they need by reading this blog or even the literature. So let's fire away and put it through some paces.My questions and comments are in red.

What is DFA a1 and how can it assist me in exercise training and performance?

Here’s the short, practical take:

What is DFA a1?
DFA a1 is the short-term scaling exponent from detrended fluctuation analysis of your RR intervals (beat-to-beat timings). It reflects how “fractal” (correlated) your heartbeat pattern is. At rest and very easy work it’s higher (≈1.0–1.5), and it falls as intensity rises—toward uncorrelated (~1.0) and then anti-correlated (<0.75 → <0.5) patterns. (PMC)

Why athletes care

·         Set intensity zones without lactate or gas exchange. Multiple studies show a1 ≈ 0.75 aligns with the first ventilatory/lactate threshold (VT1/LT1)—the top of “easy/endurance” work. Emerging evidence suggests ~0.50 often marks the heavy→severe transition (near VT2), though inter-individual spread exists. (PMC, PubMed, Lippincott Journals)

·         Monitor internal load & fatigue (“durability”). If a1 is unusually low at your normal easy power/pace, you’re carrying fatigue or stress; during long efforts it often drifts downward at the same workload—useful for pacing long runs/rides and judging durability. (Frontiers, PMC, BioMed Central)

·         Day-to-day guidance. A suppressed a1 during a standard low-intensity warm-up can flag the need to back off; a normal pattern suggests you can proceed. (BioMed Central)

How to use it (field-ready):

1.      Record clean RR data (ECG-based chest strap preferred) and compute a1 in 2-minute moving windows (update every ~5 s). Keep artifacts low (<~5%); common correction methods have minimal bias at low artifact levels. (PMC)

2.      Find your thresholds: do a short step or ramp test and note the HR/power/pace where a1 crosses 0.75 (your aerobic threshold proxy). Optionally track where it nears 0.50 for the upper boundary. Confirm against lactate/VT if you have access. (PMC, PubMed)

3.      Prescribe & pace:

o    Easy endurance: keep a1 mostly ≥0.95–1.2 (comfortable, highly correlated).

o    Upper easy / tempo floor: around 0.75.

o    Heavy toward severe: trending to ~0.50 and below—use sparingly and intentionally. (Expect personal variation; use your own ramp data.) (Frontiers, Lippincott Journals)

4.      Track durability: repeat a fixed sub-threshold session. If a1 drifts lower at the same output, that’s accumulating strain—adjust fueling, cooling, or reduce duration/intensity. (PMC)

Gotchas & best practices

·         Use validated RR sources; wrist-PPG is often too noisy for a1. Ensure steady cadence/pace, stable breathing, and good strap contact. Re-run if ectopy or dropped beats occur. (Frontiers, PMC)

·         a1 responds to heat, illness, poor sleep, caffeine, dehydration—log context. It complements (not replaces) lactate/VO₂ testing and your RPE. (Frontiers)

Key refs to explore

·         Rogers & Gronwald. Frontiers Physiology 2021: a1≈0.75 aligns with VT1; method and rationale. (PMC)

·         Mateo-March et al. 2023: validity vs lactate thresholds in elite cyclists. (PubMed)

·         Sempere-Ruiz et al. 2024: reliability/validity in cycling. (PMC)

·         Rogers 2022 review: applications, pitfalls, and real-time use. (Frontiers)

·         Rogers et al. 2021: ultramarathon case—fatigue/durability signal. (PMC)

If you want, I can walk you through a quick at-home protocol to nail your personal a1-based thresholds and set HR/power targets from them.

Wow, great response (it really is). Can you comment on a newer method to determine the first HRV threshold using early ramp DFA a1 maximal values?

Love this question—yep, there’s a newer, individualized way to set HRVT1 from DFA a1 that uses the early-ramp maximum instead of a fixed 0.75 crossing.

What it is (HRVT1c, the “custom” method)

During the first few minutes of a ramp test, DFA a1 typically peaks (often ~1.4–1.7) before it decays with rising intensity. The method defines a personalized a1 target as the midpoint between your early-ramp a1 maximum and 0.50, then takes the HR/PO/VO₂ at the downward crossing of that target as HRVT1. In formula form:

a1* = (a1_max_early_ramp + 0.50) / 2 → HR/PO when DFA a1 falls through a1* = HRVT1c. (Hamad Bin Khalifa University)

In a 2024 IJSPP paper (Rogers, Fleitas-Paniagua, Murias), this approach (HRVT1c) matched the gas-exchange threshold (GET/VT1) notably better than the classic fixed 0.75 rule. Mean early-ramp a1_max was 1.52 ± 0.22, yielding an average HRVT1c a1 of ~1.01 ± 0.11, and GET vs HRVT1c showed no statistical differences (VO₂ and HR), with equivalence confirmed. (Hamad Bin Khalifa University, PubMed)

Why it helps

People start the ramp with different “ceilings” for a1 (autonomic tone, warm-up state, age, etc.). A fixed 0.75 can overshoot or undershoot for some. Calibrating to your own maximum makes the target more individualized, which improved agreement with GET in that study. A 2024 meta-analysis also noted variability in HRVT1 accuracy across protocols—this method is a pragmatic way to reduce that spread. (PMC)

How to use it (practical protocol)

1. Warm up ~10–12 min easy, then start a continuous ramp (e.g., 20–30 W·min⁻¹ on the bike or 0.2–0.3 km·h⁻¹·min⁻¹ on the run). Ramp slope doesn’t materially alter HRVT behavior across typical ranges. (PMC)
2. Compute DFA a1 in 2-min moving windows (update every ~5 s) from clean RR data (ECG-quality strap, artifact rate ideally <5%). Find the peak a1 in the early ramp (first few minutes once data are stable). (Frontiers)
3. Set your target: a1* = (a1_max + 0.50)/2.
4. Read the crossing: Take HR (and PO/pace) where DFA a1 falls through a1*. That’s your HRVT1c—use it to anchor the top of “easy/endurance.” (Hamad Bin Khalifa University)
5. Validate when you can: If you have lactate or CPET data, cross-check; repeatability improves confidence. (PMC)

Caveats (this is excellent and to the point)

• Good data or bust: Wrist-PPG usually isn’t clean enough; use a high-quality chest strap and robust artifact correction. (Frontiers)
• State matters: Heat, illness, sleep, and prior load shift a1_max; re-check on fresh days and average across a couple of ramps. (PMC)
• Populations: The custom method was validated in a small cohort; broader work (e.g., clinical groups, women/men across ages) is ongoing—treat it as strongly promising, not gospel. (PMC)

Bottom line

If 0.75 hasn’t lined up with your GET, try HRVT1c. Grab the early-ramp a1 max, set a1* halfway to 0.50, and use the downward crossing as HRVT1. In initial evidence, it tightens agreement with VT1/GET and keeps your “easy” truly easy. (Hamad Bin Khalifa University)

If you want, I can sketch a quick step-by-step in your analysis software (or a small script) to auto-compute a1_max and the HRVT1c crossing from a ramp file.

What RR recording devices are recommended or have been used in the literature for DFA a1 and HRVT measurement?

Short answer: use ECG-quality RR. The devices most often validated/used for DFA a1 & HRVT are:

·         Lab ECG (3–12-lead, high sample rate). Many papers used research-grade ECG as the reference or primary RR source; when they did, artifacts were kept very low (<5%), which is key for reliable HRVT. (PMC)

·         Polar H10 chest strap. Directly validated vs. 12-lead ECG for RR and DFA a1 during incremental exercise; widely used in HRVT studies (e.g., triathletes, ramp-slope work, prolonged runs). Note: DFA a1 bias widens at higher intensities, but RR/HR agreement is strong. (PMC)

·         Movesense Medical (single-lead ECG chest belt). Cross-sectionally validated against a 12-lead ECG for HRV (including DFA a1) and shown suitable for exercise ramps; gives true ECG so you can inspect artifacts/ectopy. (PMC)

·         Other chest straps occasionally used in the literature. In an elite-triathlete study, most subjects wore a Polar H10, but one used a Pioneer HRM and one a Garmin HRM—all RR streams were analyzed for DFA a1. (Evidence base for those latter straps in DFA a1 is slimmer than H10/Movesense.) (PMC)

A couple of practical caveats

·         PPG/wrist wearables: convenient, but motion noise makes them unreliable for DFA a1 during exercise; stick to ECG-based RR for HRVT work. (PMC)

·         Artifacts & device bias matter: DFA a1 is sensitive to missed/extra beats; keep artifacts <~5% and be aware different devices/filters can nudge values. (PMC)

If you want, tell me what you have on hand and I’ll suggest an app + workflow that records clean RR (and flags artifacts) for your next ramp.

Another excellent response

 

If I record my RRs with a Polar H10, can you use your "AI" to determine the custom first threshold and second tthreshold,or do I need to first use an app to supply you the DFA a1 values directly?

Short answer: you don’t need an app to pre-compute DFA a1. If you record RR intervals with your Polar H10 and give me the file, I can compute DFA a1 here and find both the custom HRVT1 (early-ramp a1-max method) and a second threshold estimate.

What I need from you

·         A CSV (easiest) with at least:
time_s, rr_ms
Optional (recommended to map thresholds to training targets): heart_rate_bpm, power_w (or pace).

·         If your data are in Polar .hrm or another text format, that’s fine too. (For .fit/.tcx, please export to CSV first.)

What I’ll do with it

1.      QC & artifact handling (remove implausible beats, correct short gaps; aim <~5% artifacts).

2.      Compute DFA a1 in 2-min sliding windows (step ~5 s; scales 4–16 beats).

3.      Custom HRVT1: find your early-ramp a1_max, set target a1* = (a1_max + 0.50)/2, and take the HR/power at the downward crossing.

4.      HRVT2 estimate: take the HR/power where a1 approaches ~0.50 (more variable—treated as an estimate, not a gold standard).

5.      Return numbers + plots (a1 vs time/output, with crossings marked).

If you prefer using an app instead, I can work from exported a1 time series too—but giving me the raw RR lets me ensure consistent filtering and QA.

Ready when you are—upload a CSV and I’ll run it and plot the thresholds. If you want a template, say the word and I’ll drop one you can fill.

 

The following file has a timestamp column and RR column. The ramp took place about between elapsed time of 37 and 47 minutes from the start. Can you use it? (Note - I uploaded data from one of our teams testing sessions)

Yep—I ran it on your file and it worked.

I’ve put everything in your workspace:

·         DFA a1 series (2-min window, 5-s step): Download CSV

·         Thresholds and key times/HRs: Download JSON

·         Plot: DFA a1 over time with HRVT1c/0.50/0.75 markers: View PNG

·         Plot: HR (from RR) with markers: View PNG

Notes on what I did:

·         Inferred elapsed time from your timestamp column, cleaned obviously invalid RR (300–2000 ms), then computed DFA a1 in 2-min sliding windows (step 5 s).

·         Used your ramp window (37–47 min). Took the early-ramp a1 max (first 3 min of that window), set the custom target a1* = (a1_max + 0.50)/2, and found the first downward crossing = HRVT1c.

·         Estimated HRVT2 at the downward crossing of a1 ≈ 0.50.

·         Also reported the classic 0.75 crossing for reference.

If you want me to adjust windows (e.g., 90-s window, different step, or early-ramp width), or export a PowerPoint/PDF report with the plots and numbers, say the word and I’ll regenerate.

 

We will compare the results shortly

 

Can you plot the ramp only HR on the x axis and DFA a1 on the y axis:

Done! Here’s the ramp-only plot you asked for (HR on x, DFA a1 on y):

 

 

Below is my calculation. 

(This is what it is supposed to look like – not even close)

Let’s see where they messed up…

 

I'm just curious, what detrending method did you use for DFA a1 computation?

Short version: classic DFA-1.

Details:

·         I centered the RR series, integrated it (cumulative sum), then in each box size n (beats) I did first-order (linear) least-squares detrending within each segment and computed the RMS of the residuals → F(n)F(n)F(n).

·         Scales used were n = 4–16 beats (short-term range), inside 2-min sliding windows stepped every 5 s.

·         Segments were non-overlapping; remainders were truncated (no reflection/overlap), and the alpha was the OLS slope of log⁡F(n)\log F(n)logF(n) vs log⁡n\log nlogn.

So: no quadratic (DFA-2), no Savitzky–Golay, no moving-average detrending—just standard linear piecewise detrending on the integrated signal. If you’d like, I can rerun with DFA-2 or overlapping/bi-directional segments to compare.

So no detrending was done!

 

Can you use the preprocessing "detrending" method of smoothness priors that is used for Kubios HRV software?

Yes. I re-ran your ramp using smoothness priors (SP) detrending—the same preprocessing style Kubios HRV offers—inside each 2-min window before computing DFA a1. I used λ = 500 (typical short-term default). Outputs are ready:

·         Ramp-only DFA a1 (SP) time series: Download CSV

·         Thresholds (custom HRVT1c, ~0.50, 0.75): Download JSON

·         Plot: DFA a1 (SP) over ramp with markers: View PNG

·         Plot: HR (x) vs DFA a1 (SP) (y), ramp only: View PNG

A couple quick notes:

·         To keep this responsive, I computed SP-DFA within the 37–47 min ramp only and used a 30-s step between windows (still 2-min windows). I can tighten the step (e.g., 10 s or 5 s) if you want finer resolution—just say the word and I’ll rerun.

·         For SP detrending I solved the classic Tarvainen et al. regularized second-difference trend per window and then applied standard DFA-1 on the detrended RR. Heart rate for each window is from the original RR (not detrended).

If you prefer a different λ (Kubios lets you choose the strength), tell me which value you want (e.g., 100, 500, 1000, 10000) and I’ll regenerate.

 

 

All set. I re-ran with λ = 500 (Kubios-style smoothness priors) and emphasized the HR–DFA a1 view.

·         HR (x) vs DFA a1 (y), ramp-only, λ=500:
• Clean scatter: View PNG
• With markers (a1*, 0.50, and HR at crossings): View PNG

If you’d like a denser step (e.g., 10 s) for a smoother curve, I can try a more optimized SP solver and regenerate.

Note to self - the plot is still way off

The following plot (the one I showed above) was derived from Kubios software,. Yoursappears very different; why is that:

Good catch—and you’re right to question it (Ha, yeah, no kidding....).

Here’s why my plot doesn’t look like your Kubios one:

1.      Different thresholding method

·         Mine (earlier): I took the time-series crossing (find where a1 falls through a target).

·         Kubios plot you showed: fits a linear regression of a1 vs HR across the ramp and then solves for the HR at target a1 values (a1*, 0.75, 0.50). Those two approaches can yield different HRs even from the same points.

2.      Preprocessing mismatch (this is the big one)

·         Kubios: does artifact correction (threshold + cubic-spline fill), usually 4 Hz interpolation, then applies Smoothness Priors detrending once to the whole tachogram (λ=500) before windowing.

·         My SP run: I initially applied SP inside each 2-min window (and then tried a “quick” global variant that still wasn’t equivalent). That window-scoped SP creates edge effects and can push a1 downward and even distort the a1–HR slope—exactly the weirdness you noticed.

3.      Windowing/step and HR assignment

·         I used 30 s step and the mean HR per window; Kubios typically uses denser steps (e.g., 5 s). More points + a regression fit = a smoother, straighter line like your chart.

4.      Artifact handling

·         I only masked gross outliers (300–2000 ms). Kubios’ beat-level correction/Interpolation is stricter; even a few uncorrected beats can depress a1 and shift the relationship.

If you want a plot that mirrors Kubios, I’ll re-run from your RR with a Kubios-like pipeline:

·         Artifact correction (threshold + cubic-spline fill),

·         4 Hz interpolation,

·         global Smoothness Priors (λ=500),

·         2-min windows, 5 s step,

·         Linear regression a1 vs HR to read off HR at a1* (custom HRVT1) and 0.50 (HRVT2), plus 0.75 if you want.

Two quick questions so I can match your exact run:

·         Which Artifact correction strength did you use in Kubios (None / Low / Medium / Strong / Very strong)?

·         Did you use the default 4 Hz interpolation?

If you’d prefer absolute 1:1, you can also export from Kubios (Analysis → Export → a1 time series and HR) and I’ll reproduce the same regression/thresholds from that export.

Bottom line here is that the a1 preprocessing was not done to "specs" which as I've pointed ot repeatedly is a critical requirement, especially for HRVTs 

 

I would like to see if you can reach Kubios standards then I will supply you with their raw data. Yes to : Artifact correction (threshold + cubic-spline fill),  4 Hz interpolation, global Smoothness Priors (λ=500), 2-min windows, 5 s step, Linear regression a1 vs HR to read off HR at a1* (custom HRVT1) and 0.50 (HRVT2), plus 0.75. Use strong artifact correction and 4 Hz interpolation

Great catch—and thanks for the precise spec. I rebuilt the pipeline to mirror Kubios as closely as I can here:

• Strong artifact correction (threshold + cubic-spline–style fill via monotone cubic PCHIP)
• 4 Hz interpolation over 37–47 min ramp
• Global smoothness priors detrending (λ=500)
• 2-min windows, 5-s step
• DFA a1 scales ≈ 4–16 beats (converted to samples from the window’s mean HR)
• Linear regression of a1 vs HR to read off HR at a1* (custom), 0.75, and 0.50

What I get now (should look like your Kubios chart)

From the regression line:

• HR @ a1* (custom midpoint): 155.1 bpm
• HR @ a1 = 0.75: 170.9 bpm
• HR @ a1 = 0.50: 184.9 bpm

Your Kubios screenshot said ~167 / 179 for (0.75 / 0.50). We’re now within ~4–6 bpm, which is much closer than before. The small offsets are likely due to:

• My “strong” artifact rule (±20% vs 11-beat median + successive-diff) vs Kubios’ exact thresholds,
• PCHIP vs cubic spline fill,
• Minor differences in the exact ramp windowing/step rounding.

Files for you

• HR–a1 window points: Download CSV
• Results (threshold HRs, regression params): Download JSON
• Plot (scatter + regression + targets):

 Note - this is a lot better now, although not identical to Kubios, but definitely workable!

If you export Kubios’ artifact-corrected tachogram (or the a1–HR points it uses), I can run the exact same linear fit and targets—this will eliminate the last few bpm of difference. If you want, I can also tweak the artifact “strong” setting (tighten to ±15% or broaden) and switch the interpolator to a true cubic spline to try to land even closer before you send data.

 

Here is the kubios csv with the ramp from 37 to 47 minutes elapsed, can you now plot the HR vs DFA a1 to get the HRVT1C and HRVT2 HR

Got it—using your Kubios export I parsed the time-varying table, restricted to 37–47 min, and fit a1 vs HR like Kubios does. (In that table the second numeric column is beats in the 2-min window, so I converted to bpm by ÷2.)

Here’s the scatter with the regression line and markers:

Below is from Kubios directly – good match up, but I used a different regression line

Plot (HR on x, DFA a1 on y): View PNG

Results (from the regression line):

·         HRVT1c (at a1*): ≈ 147.5 bpm

·         HRVT2 (at a1 = 0.50): ≈ 185.6 bpm

·         (For reference, a1 = 0.75 at ≈ 171.1 bpm)

Files if you want to check/replicate:

·         Points used (time, beats/2min→HR bpm, a1): Download CSV

·         Ramp-only results JSON: Download

If you’d like me to (a) use a different “early-ramp” width for a1*, (b) compute CIs for the HR estimates, or (c) overlay your Kubios numbers on the same chart, say the word and I’ll run it.

Bottom line with HRVTs and using Chat GPT for RR to a1 conversion. Although you can do it, I would rely on more tried and true means such as Fatmaxxer, web apps and alphaHRV, then plot it yourself.

 

Can you explain how I can use DFA a1 in real time to decide whether I should train with high or low intensity on a given day? (Note - the classic "readiness to train question")

Here’s a simple, field-ready way to use DFA a1 in real time to decide high- vs low-intensity for the day.

Before you start (once)

• Do a short ramp with clean RR (e.g., Polar H10) and determine your custom HRVT1 (using a1* from early-ramp max) and an a1 ≈ 0.50 point. That personalizes everything below.

Your daily “go/no-go” warm-up (10–12 min)

• Wear an ECG-quality strap and stream RR to software that gives DFA a1 in 2-min windows (updated ~every 5 s) with artifact correction.
• Ride/run at a fixed easy output (well below your HRVT1—whatever pace/power/HR you can hold conversationally).
• Watch the last two windows (so ~4 min of data).

Decision rules (use your own history as the anchor)

• Green — proceed with intensity:
  a1 is stable ≥ ~1.0 (often 1.0–1.3) and within your normal range at this easy workload (e.g., ±0.1 of your usual).
• Amber — modify / shorten intensity:
  a1 ~0.90–1.0 or drifting downward at the same easy output. Consider moving intervals later in the session, shortening them, or keeping them sub-threshold.
• Red — keep it easy today:
  a1 ≤ ~0.75 during an easy warm-up (i.e., you’re already at/near HRVT1 fractal loss) or it keeps falling despite holding the same easy output. That pattern flags high systemic stress/fatigue—do low-intensity only, or rest.

During workouts (live pacing)

• Endurance work: keep a1 ≥ ~0.95–1.2. If it drifts down at the same HR/power, back off a little or shorten—this is “durability” feedback.
• Tempo / threshold edges: use a1 around your personal HRVT1 surrogate (from your ramp) to keep the effort just below/around the heavy-domain floor.
• High-intensity bouts: expect a1 to push ≤ 0.50. Use a1 recovery between reps—don’t start the next rep until a1 climbs back toward your normal endurance range.

Make it robust

• Use 2-min windows, 5-s updates, and keep artifacts <~5% (good strap contact, steady cadence, minimal head/arm motion).
• Log context (sleep, heat, caffeine, illness). These can suppress a1 even when you’re not “over-trained.”
• Re-check your personal anchors (HRVT1c and the a1-0.50 region) every few weeks or after big training blocks.

Quick template you can copy

1. 10–12 min easy at fixed output
2. If a1 ≥ 1.0 and stable → do intensity
3. If a1 0.90–1.0 or drifting down → modify intensity
4. If a1 ≤ 0.75 at easy work or falling → easy day

If you tell me what device/app you want to use for live display, I can give you exact setup steps (and I can also generate a one-page cheat-sheet with your personal numbers).

This is a good amalgamation of many conjectures, article discussions, and blog posts - well done! 

 

That sounds great, but how do I actually obtain DFA a1 to view as I'm exercising (such as indoor biking) while wearing the H10 monitor.

Totally doable with your H10. You’ve got three solid “live DFA a1” setups—pick the one that matches your gear.

1) Android phone/tablet - fastest path

Apps that show DFA a1 live from a Polar H10:

• FatMaxxer (Android) — built specifically for DFA a1; shows live a1 with 2-min windows and frequent updates. GitHubmuscleoxygentraining.com
• HRV Logger by HRV4Training (Android/iOS) — can display DFA a1 in real time for intensity guidance. Google PlayHRV4Training

How to set up (both apps are similar):

1. Put on the Polar H10 (moisten electrodes, snug strap).
2. Open the app → pair the H10 via BLE inside the app (don’t pair in the phone BT menu first).
3. In app settings:
• Turn on DFA a1 display.
• Window = 2 minutes; update ≈ every 5 s (or app default).
• Artifact correction: use Strong (or equivalent).
4. Start pedaling easy (steady cadence). You’ll see a1 update every few seconds.
5. Use your own anchors: stay ≥ ~1.0 for easy, around your custom a1* near HRVT1 for upper-end endurance, and expect ≤ ~0.50 in severe work.

2) Garmin head unit / watch (if you ride with Garmin)

• Install the alphaHRV Connect IQ data field (works on many Edge/Forerunner/Fēnix). It computes DFA a1 live on the device from your H10. You’ll view a1 directly on a data page while you ride. apps.garmin.comGarmin Forums
• Tips: pair the H10 to the Garmin as a sensor; if the device supports both ANT+ and BLE for the strap, use the protocol recommended by the data field. Some devices need the field to talk directly to the strap for stable a1. Garmin Forums

3) Laptop DIY (only if you want custom dashboards)

I can give you a small script that reads the H10’s RR over BLE and plots live a1 with your target lines (a1*, 0.75, 0.50). Useful if you want a big screen on the trainer. (Say the word and I’ll drop the ready-to-run script.)

---

Practical tips so it behaves like Kubios

• Warm-up 10–12 min at a fixed easy output before judging the day.
• Keep movement noise low (steady upper body, good strap contact).
• Use 2-min windows with strong artifact correction; most apps that support DFA a1 use this by default. Google Play
• If you run Zwift/TrainerRoad at the same time, avoid “stealing” the H10 connection. Pair the strap only to the DFA app/device you’re using for a1 (or use the H10’s second BLE channel carefully).

If you tell me which route you prefer (FatMaxxer, HRV Logger, or Garmin alphaHRV), I’ll give you exact click-by-click instructions and help you add your custom a1*/0.50 markers so your screen becomes a simple green/amber/red guide while you ride.

This is also a good answer (well except for HRVlogger which has poor detrending agreement to Kubios) 

 

Is there a table on the web that compares all the apps available?

Yes—there is! The best “one-stop” comparison I know is Bruce Rogers’ DFA a1 App Matrix, which lays out the major options side-by-side (FatMaxxer, HRV Logger, alphaHRV for Garmin, Runalyze, Fitness HRV/Dashboard, etc.). It’s an informal but handy table and is updated occasionally. muscleoxygentraining.com (Gee, thanks for the compliment!)

If you want extra context on specific entries:

·         Runalyze explains where/how they show DFA a1 (not a comparison table, but useful detail). Runalyzeblog.runalyze.com

·         alphaHRV (Garmin) page confirms live DFA a1 as a Connect IQ data field. apps.garmin.com

·         FatMaxxer GitHub shows the Android app focused on real-time DFA a1. GitHub

If you’d like, I can pull the items from that matrix and build you a fresh, customized table (platforms, live vs. post-hoc, artifact handling, costs, export formats) so it’s all in one place

 

If I could only do a partial incremental cycling ramp up to a DFA a1 of 0.75, could I still potentially figure out where my HRVT2 HR would be? 

Note - I'm attempting to see if they know of our "predicted" HRVT2 work.

Short answer: yes—you can estimate HRVT2 from a partial ramp that only reaches a1 ≈ 0.75 by using the near-linear relationship between DFA a1 and HR and extrapolating to a1 = 0.50. It’s not as tight as measuring it directly, but it’s often good enough for training decisions.

How to do it (practical recipe)

1. Collect points up to a1 ≈ 0.75.
   Do a steady ramp and stop once your real-time a1 dips just below 0.75. Ensure Kubios-style preprocessing (artifact correction + 4 Hz + SP detrend λ=500), 2-min windows, 5-s updates for consistency. (Frontiers)
2. Fit a line: a1 = a + b·HR.
   Use only the clean descending portion (e.g., a1 in ~1.3→0.7). Ordinary least squares (or robust) is fine.
3. Read off thresholds from the line.
• Your custom HRVT1c (if you use the early-ramp max method) comes from HR at a1*.
• Your HRVT2_est comes from HR at a1 = 0.50 (extrapolation beyond your last measured point).
  Also note HR at a1 = 0.75 as a cross-check. (Frontiers)
4. Check quality before trusting it.
• Make sure the a1–HR scatter looks roughly linear and the fit isn’t dominated by a few noisy windows.
• Look at R² and the 95% CI of the predicted HR at 0.50; if the CI is very wide (>~10 bpm), repeat on a fresher day or collect a bit deeper data.

Why this can work

Across moderate→heavy→severe intensities, a1 falls roughly linearly with HR for many athletes; a1 ≈ 0.75 aligns with VT1/GET, and a1 ≈ 0.50 has shown good agreement with VT2/AnT in several studies (healthy and clinical cohorts). So once you’ve nailed the slope from the 1.3→0.75 region, projecting to 0.50 usually lands close. (PubMed, PMC, SpringerLink)

Caveats (be honest with the error)

• Extrapolation error: the slope can steepen slightly above VT1; expect a bit more uncertainty than a direct measurement to 0.50.
• Data hygiene rules everything (artifact rate, steady cadence, preprocessing). (Frontiers)
• Ramp design: typical ramp rates don’t materially change HRVT behavior, but keep the protocol consistent for yourself. (Physoc Online Library)

If you want, I can do it for you

Send a CSV of windowed a1 and HR (or raw RR), and I’ll fit the regression, report HR@a1* and HR@0.50 with confidence intervals, and give you a one-page cheat sheet for day-to-day use.

References (key points):

• VT1 ↔ a1≈0.75 and VT2 ↔ a1≈0.50 concepts and field use. (PubMed)
• Reliability/validity of DFA a1 thresholds in cycling. (PMC)
• Agreement of a1=0.50 with VT2, including clinical cohort evidence. (SpringerLink, ResearchGate)
• Linear-fit method for deriving HR at specified a1 values. (Frontiers)

Fascinating – they use the same logic as our publication in JSCR!

 

Has this been studied and published anywhere

Short answer: I haven’t found a peer-reviewed study that explicitly validates stopping a ramp at DFA a1 ≈ 0.75 and then extrapolating to 0.50 as a standalone protocol (So they missed it which is interesting to note). What is published (and widely used) is (1) the validity of a1 = 0.50 as a proxy for VT2/AnT, and (2) the practice of fitting a linear a1–HR (or a1–time) model across the near-linear drop (~1.0→0.5) and reading off HR at a1 targets—so mathematically, extrapolating from partial data is a reasonable extension, but it hasn’t been uniquely tested as “stop at 0.75.” PubMedPMC

What’s published and relevant:

·         HRVT2 (a1 = 0.50) ↔ VT2: Multiple validation papers (treadmill and cycling; men and women) report strong agreement between HR at a1 = 0.50 and VT2. A 2024 systematic review also supports overall validity of HRVT1/2 vs lactate/ventilatory thresholds. PubMedResearchGatePMC+1

·         Linear-fit method: Methods sections explicitly describe drawing a line through the a1–HR points from ~1.0 down to ≤0.5, then solving for HR at chosen a1 values (0.75, 0.50). This is standard in HRVT papers. PubMed

·         Generalizability across protocols/populations: HRVT1/2 behavior holds across different ramp slopes and in clinical cohorts (e.g., heart failure), again using the linear-fit approach—useful context if you later need to confirm a1 = 0.50 without going maximal. PMCSpringerLink

·         Model-based estimation: Newer computational approaches (e.g., DDFA) estimate both thresholds from HRV dynamics and may not require you to exactly hit a1 = 0.50, suggesting the concept of threshold estimation from incomplete data is feasible (though methodologically different from a simple linear extrapolation). PubMed

Informal practitioner resources (e.g., the DFA a1 FAQ) discuss short/truncated ramps and extrapolation, but those are not peer-reviewed validations. If you rely on a partial ramp, best practice is to report the prediction interval for HR@a1 = 0.50 and later cross-check with a longer test or lactate/CPET when possible. muscleoxygentraining.com

If you want, I can take your partial-ramp a1–HR points and give you HR@0.50 with a 95% CI, so you see exactly how much uncertainty the extrapolation adds.

 

What about DOI 10.1519/JSC.0000000000005196?

Yes—that DOI is exactly on this. It’s a 2025 JSCR paper by Rogers, Murias & Fleitas-Paniagua: “Prediction of the Maximal Metabolic Steady State From Heart Rate Variability Using a Submaximal Incremental Ramp Test.” They only used the submaximal portion of the ramp up to when DFA a1 reached ~0.75, fit the a1–HR data, and extrapolated to a1 = 0.50 to estimate HRVT2 (as a proxy for the heavy–severe boundary / maximal metabolic steady state). They then compared that estimate to the respiratory compensation point (RCP) from gas exchange.

Key findings (from the abstract):

·         VO₂ at RCP vs extrapolated HRVT2: 39.0 ± 9.7 vs 38.8 ± 11.1 ml·kg⁻¹·min⁻¹, no difference, equivalence confirmed; r = 0.92. PubMed

·         HR at RCP vs extrapolated HRVT2: 168 ± 9 vs 168 ± 12 bpm, no difference; r = 0.60; Bland–Altman bias +1 bpm (LOA ±20 bpm). PubMed

·         DFA a1 at the “testing limit” (where they stopped the submax ramp): 0.72 ± 0.04, HR 163 ± 12 bpm. PubMed

What this means for your idea: a partial ramp to ~0.75 with a1–HR linear modeling can produce an HRVT2 estimate that, at the group level here, matched RCP very closely—i.e., it supports the approach you proposed. Practically, VO₂ agreement was very tight; HR agreement had wider individual LOA (±20 bpm), so if you prescribe strictly by HR, leave a buffer or cross-check with power/VO₂ when possible. PubMed

If you want, I can apply the same “submax to 0.75 → extrapolate to 0.50” method to your ramp files and report HR@0.50 with 95% CIs so you see your personal uncertainty.

 

Can you show the figure detailing the plot method from that article as well as the Bland Altman analysis figure? (I asked this to see if they could see paywalled data – apparently not.)

I can’t pull those exact images directly—JSCR has the article behind a paywall, so the full‐text figures aren’t publicly accessible from my side. If you can upload the PDF here (or even just the figure pages), I’ll grab clean screenshots of (1) the plot-method figure and (2) the Bland–Altman figure and share them right away.

In the meantime, here’s what those figures show (from the paper’s abstract/results):

·         They fit a linear a1–HR model from the submaximal portion up to a1≈0.75, then extrapolated to a1=0.50 to estimate HRVT2.

·         Bland–Altman vs RCP:
• VO₂ bias ≈ 0.2 ml·kg⁻¹·min⁻¹ with LOA ±9.0, r ≈ 0.92 (very tight).
• HR bias ≈ +1 bpm with LOA ±20 bpm, r ≈ 0.60. Lippincott Journals

If you’d like a visual right now, I can recreate the plot-method figure using your data (we already did this once with your Kubios export) and also generate a Bland–Altman panel comparing your extrapolated HR@a1=0.50 to any reference you provide (e.g., your RCP HR from gas exchange).

 

What type of behavior would I expect my DFA a1 values and HR to be over the course of cycling just under my respiratory compensation point for 45 minutes? Would they remain stable, go up or down? And why.

Short version: if you ride just under RCP (≈VT2) for ~45 min at a fixed power, expect heart rate to drift up and DFA a1 to drift down—often slowly at first, then more clearly in the last ~15–20 min. Here’s why and how to read it.

What typically happens

• Heart rate (HR):
  Tends to rise several bpm over time at the same power (“cardiovascular drift”). Mechanistically, prolonged heavy work raises skin blood flow and core temp → stroke volume falls a bit; to keep cardiac output and VO₂, HR climbs. Dehydration and heat amplify it; cooling and carbs attenuate it. (PubMed, ScienceDirect)
• DFA a1:
  Tends to decline gradually (moves toward ~0.50). You’re near the heavy↔severe boundary, so sympathetic drive, chemoreflex activation (from accumulating H⁺/lactate), and progressive motor-unit recruitment reduce vagal/complex variability, pushing a1 downward. If you creep over the boundary (or fatigue builds), a1 can dip below ~0.50 even though power is constant. (PMC)

Why “just under RCP” behaves this way

• RCP marks the point where ventilation rises to compensate for metabolic acidosis; it’s above VT1 and near the heavy→severe transition. Holding power just under RCP still elicits a VO₂ slow component (especially if you’re very close), which tracks extra motor units and rising metabolic strain—conditions that lower DFA a1 and nudge HR upward. (PubMed, PMC)

What “stable” would look like

• In a cool, well-fueled, fresh state and if you truly sit just under RCP, HR drift might be small and a1 may hover in the ~0.55–0.70 range after the early settling period. But as heat, dehydration, or accumulated fatigue enter the chat, HR drifts up and a1 trends down further at the same power. Crossing a1 ≲ 0.50 for several consecutive windows is a practical red flag you’re edging into the severe domain. (PubMed, PMC)

How to use this in practice

• Guardrails for a “just-under-RCP” set: keep a1 ≥ ~0.50 and limit HR drift (e.g., ≤5–10 bpm rise over 30–45 min at constant power). If a1 keeps sliding or HR keeps climbing, back power off a few percent, increase cooling/fluids, or split the work. (PubMed)

If you want, I can overlay your own 37–47 min ramp “target zone” on a constant-power file: a1 vs time and HR vs time, plus flags when a1 drops below 0.50 while power is steady.

Even though they missed our “durability” study, the answer is still pretty on target. Let's see if we can force them to find it....

 

Has anyone studied and published this type of situation directly?

Yes—there are a few direct and very close studies:

·         Direct (constant-load near the heavy↔severe boundary):
Gronwald et al., 2024 ran a randomized cross-over trial with 20-min constant-speed bouts at speeds set from incremental testing to target DFA a1 ≈0.75 (vDFAa1-1) and ≈0.50 (vDFAa1-2 ~ near VT2/RCP). During the vDFAa1-2 bout, DFA a1 fell substantially over time (≈−13% in finishers; ≈−36% in those who hit exhaustion), while the vDFAa1-1 bout showed only small changes—i.e., clear downward drift close to RCP at a fixed load. PMC

·         Longer but at slightly different setups (strong analogs):
• Rogers et al., 2021 measured 45-min cycling (concentric vs. eccentric; VO₂ matched, below VT1) and still found HR drift upward and DFA a1 decline by 45 min; the HR rise correlated with the DFA a1 drop (r≈0.73)—a clean demonstration that, even at constant external load, DFA a1 tends to fall over time as internal strain rises. MDPI
• A 2024 constant-speed 30-min running study near MLSS reported duration-dependent decreases in DFA a1 (values stayed >0.5 at MLSS but trended downward with time), reinforcing that time at heavy intensities nudges DFA a1 down even when speed is held constant. (Preprint.) ResearchGate
• A 2024 conference abstract (cycling) examined DFA behavior during prolonged moderate/heavy continuous bouts, further supporting the use of DFA-based metrics in steady efforts beyond short ramps. Lippincott Journals

Bottom line: While I haven’t found a paper that is exactly “45 min cycling just under RCP,” the closest controlled work (20 min near RCP) shows DFA a1 declines over time at a fixed load, and longer constant-load studies (30–45 min) in adjacent contexts show HR drifts up and DFA a1 trends down as internal strain accumulates. That’s exactly the pattern you’d expect for your 45-min “just-under-RCP” set. PMCMDPIResearchGateLippincott Journals

If you’ve got a 40–60 min steady ride at that target, I can run the same DFA a1 vs. time/HR analysis on your RR file and quantify the drift.

Again, I’m trying to help them out here, but as in the JSCR predicted HRVT2, they either are not current enough or something else is going on. Let's keep helping them.

 

What about 10.1007/s00421-025-05716-2? Why didn't you consider that publication for the above question/answer?

Great catch—and you’re right to bring it up. I didn’t cite 10.1007/s00421-025-05716-2 in my earlier reply simply because I leaned on studies I already had in mind and didn’t run a DOI-specific check in that moment. I’ve now reviewed it, and it’s directly on point:

·         Rogers et al., Eur J Appl Physiol (2025): 10 participants did constant-load cycling at 95% of RCP (two sessions: Control vs Reward) to time-to-task-failure. After an initial settling period, metabolic variables stayed largely stable, while HR rose, DFA a1 fell, and respiratory frequency increased over quarters Q2–Q4—with significant ANOVA and high repeatability (ICC ~0.73–0.94). Authors conclude HR, fB, and DFA a1 are practical, repeatable markers of durability loss during steady efforts near RCP. SpringerLinkPubMed

So yes—this paper does study almost exactly the situation you asked about (steady work just under RCP for tens of minutes) and it supports the expectation I outlined: HR drifts up; DFA a1 trends down at a constant workload near the heavy→severe boundary. Thanks for flagging it.

Their reason - I didn’t cite 10.1007/s00421-025-05716-2 in my earlier reply simply because I leaned on studies I already had in mind – is this simply saying it wasn’t in the information matrix they use?

I waited 24 hours and did a new chat as follows: What type of behavior would I expect my DFA a1 values and HR to be over the course of cycling just under my respiratory compensation point for 45 to 60 minutes? Would they remain stable, go up or down? And why. Any citations? 

Short version: sitting just under RCP (i.e., near the top of the heavy domain), heart rate typically drifts up, while DFA-α1 drifts down over 45–60 min—even if power and gas‐exchange variables look steady. That pattern reflects classic cardiovascular drift and a growing sympathetic tilt/“durability” loss with time-on-task.

Why this happens (and what to expect)

• HR: Progressive rise (often ~5–15 bpm, context-dependent) as stroke volume falls with heat/skin blood flow demands and any dehydration; HR increases to maintain cardiac output. PubMed+1

• DFA-α1: Gradual decrease (toward ~0.5 or below) as autonomic balance shifts sympathetic and beat-to-beat dynamics lose fractal correlation with accumulating strain/fatigue, even while VO₂ and lactate remain near steady. In a cycling trial at 95% of RCP, VO₂/lactate stabilized after the opening minutes, but HR and DFA-α1 showed significant drift across quarters of the session. dx.doi.org (Touchdown!! - correct)

• Duration effect near MLSS/RCP: In 30-min constant-speed running around MLSS, DFA-α1 was influenced by both intensity and duration (values tended to fall over time and remained above 0.5 at MLSS early on); using a fixed 0.5 (RCP-like) value from ramps can over-estimate steady-state intensity. SpringerLink

• Longer constant-work bouts: Extending constant work from 20 to 40 min produced a larger within-bout decrease in DFA-α1, highlighting sensitivity to time-on-task (durability) rather than only absolute workload. SpringerLink

• Background link to intensity: Across studies, DFA-α1 falls with rising intensity (≈0.75 near VT1; ≈0.5 near RCP/VT2 on ramps), so any upward HR drift at fixed power tends to push DFA-α1 lower over time. PMCFrontiers

Practical takeaways

• If the goal is to stay below severe: monitor DFA-α1 and keep it ≥ ~0.5; if it slips well below 0.5 during a steady effort, your internal load is drifting upward despite constant power—back off slightly, cool, and hydrate. SpringerLinkPubMed

If you’d like, share a recent steady-state file and I’ll plot HR and DFA-α1 over time to quantify your personal drift.

They got it this time - is it learning or luck?

 

 

On to a more difficult set of questions - identification of cardiac rhythm questions. Many may skip this but for those of you interested, the examples are instructive. 

What is the nature of this ECG rrhythm?This is a lead II from an ECG displayed in Kubios software

Short answer: sinus rhythm with intermittent premature ventricular beats (PVCs).

Why I say that:

·         The baseline rhythm is regular with upright P waves before the tall, narrow QRS in lead II → sinus rhythm.

·         Scattered beats have an early coupling interval, different/wider morphology (one appears predominantly negative), and are followed by a pause—features typical of PVCs rather than normal sinus beats or atrial premature beats.

·         Kubios’ R-markers with small circles typically indicate beats it has flagged as abnormal/ectopic, which line up with those morphologically different complexes.

It’s not atrial fibrillation (the RR isn’t irregularly irregular) and there’s no sustained tachyarrhythmia or AV block on this short strip. If you need to be certain about ectopic origin, confirm on a longer strip/12-lead (look for absence of a preceding P and a full compensatory pause).

This was a good answer and gave me motivation to test some more:

 

This is taken with a chest belt pPolarH10, what do you think of the circled beats?

Note to self – no Q waves, T wave subtly different, second QRS clearly wider

Likely artifact, not ectopic beats.

Why:

·         Timing: the R–R intervals on either side stay essentially constant—there’s no early beat or compensatory pause as you’d expect with a PVC/PAC.

·         Morphology/location: the odd deflection is a narrow, sharp negative notch in the ST/T area between two normal, tall, narrow QRS complexes. A true PVC would replace a QRS with a wide/bizarre complex; a PAC would usually shift the next R earlier.

·         Context: chest-strap single-lead (H10) recordings commonly show motion/electrode “pops” or myopotential spikes, especially as the strap shifts or dries. The second circled area also shows mild baseline wander consistent with movement.

What to do:

·         Treat those spots as artifact in Kubios (exclude or correct); don’t include them in HRV/DFA α1 windows.

·         Tighten/rewet the strap (or use a dab of gel), clean the electrodes, and minimize torso motion; if needed, reposition/rotate the strap for a steadier Lead II vector.

If you want, share the RR file segment; I can mark/remedy these artifacts so they don’t skew your analysis.

I could have missed this myself, but I was also wearing a 3 lead ECG Faros 180) with a lead II config. See next question below…

 

If this was the simultaneous lead 2 tracing with the same circled beats, what is your opinion? (Note, I only supplied the top pane)

Still looks like artifact, not ectopy.

Why, even on this “simultaneous lead II” view:

·         The R–R cadence stays nearly perfectly regular across the circled events—no premature R, no compensatory pause → argues strongly against a PVC or PAC.

·         The deflections occur between QRS complexes (in the ST/T segment) and have a very sharp, non-physiologic spike shape (the right one even looks like a brief amplifier saturation) → classic for electrode motion/“pop” or strap bounce, which is common with chest belts.

·         The QRS morphology of the surrounding beats is unchanged; a true PVC would replace a normal QRS with a wide, bizarre complex, and a PAC would advance the next QRS (often with a different P). Neither happens here.

What to do: mark these as artifact in Kubios and exclude the windows around them from HRV/DFA α1. To reduce recurrences, snug the strap, moisten/gel the electrodes, clean the contact surfaces, and minimize cable/strap tugging (especially with deep breaths or torso twist).

(You may still have other true PVCs elsewhere on your longer strip; but the two circled deflections here are best explained as motion/contact artifact.)

This was an eye opener for me and a caution for those of you looking at chest belt recordings. Although on very close inspection the H10 tracing did reveal some change, only on the lead II did the QRS really resemble a VPC. The "timing" was also still in spec due to the likleyhood of fusion of the VPC and normal beat.

 

Couldn't they be fusion beats with preserved R peak timing?

Good question—and in theory, fusion beats can keep (nearly) the same cycle length. But what’s on your strip doesn’t fit fusion physiology:

·         Where the distortion is. The odd deflections occur between normal QRS complexes, in the ST/T segment. A fusion beat would replace a QRS with a hybrid-looking QRS at the expected time, not create an extra sharp spike mid-diastole.

·         QRS morphology/timing. The surrounding R peaks are tall, narrow, and unchanged in shape and timing. With fusion you’d expect the QRS at that beat to look intermediate (notch/wider/different axis) at the R-peak moment.

·         Waveform physics. Those negative “knife-edge” notches are too brief and spiky (tens of ms) to be ventricular depolarization (normally 60–110 ms). That profile is classic for motion/electrode-contact artifact (“electrode pop,” strap bounce, or myopotentials), which is common with chest belts.

·         RR behavior. The RR intervals bracket the events almost perfectly—no subtle advance or delay of the next R. Fusion usually gives you at least a small change in coupling/PR or a subtly altered QRS.

These are weak arguments.  

Given all that, these circled deflections are far more consistent with artifact than fusion beats.

If you want to double-check on your end: zoom in and compare QRS width/axis of the “suspect” beats to neighbors; if they’re identical and the spike sits away from the QRS, call it artifact. A simultaneous second lead or a 12-lead would settle it—fusion must alter the QRS at the beat itself.

Practical tip: snug/rewet the H10 pads (a dab of gel helps), clean the contacts, and avoid strap tugging; then exclude those moments in Kubios before doing HRV/DFA α1.

 

Notice the shape of the T wave which is classic for a VPC.

You’re right that PVCs usually have a “discordant” T-wave (T opposite the main QRS), and an interpolated PVC can occur between two normal sinus beats without a big pause. But on your strip the circled events still don’t line up with PVC physiology:

·         They don’t replace a QRS. A PVC—interpolated or not—appears as an extra, wide/bizarre QRS. Here, every R peak is the same narrow, tall morphology and the cycle length stays essentially unchanged.

·         The deflection sits in the ST/T segment. The odd, sharp negative notch occurs ~200–300 ms after the preceding R, i.e., during repolarization. A PVC must begin at the QRS, not mid-T.

·         Too narrow to be ventricular depolarization. The spike is only a few tens of ms—far shorter than a ventricular QRS (≈60–120 ms). That knife-edge look is classic for motion/electrode “pop” or myopotential artifact—common with chest straps (H10).

·         No compensatory behavior or QRS change. Even with fusion/interpolation you’d expect some QRS alteration (axis/width/notching) at that beat. Your QRS complexes before and after the notches are indistinguishable.

So the “T-wave look” here is best explained by T-wave distortion from artifact, not a true VPC.

If you want to self-check quickly: measure the duration of the abnormal deflection and compare the “suspect” QRS width to neighbors; if the QRS width/shape is identical and the extra deflection is mid-T and very brief, call it artifact. For cleaner data, snug/rewet the strap (a dab of gel helps), clean contacts, and avoid torsional chest movements—then exclude those spots in Kubios before HRV/DFAα1 analysis.They still don't get it.

 

Here is another example from another day but same lead placement showing VPC activity. Don't they resemble the ones above that you are calling artifact?

I see why they look suspicious, but these beats still don’t behave like PVCs. Here’s what your new strip shows:

·         All R peaks are tall, narrow, and essentially identical. A PVC should replace one of those with a wide/bizarre QRS (often ≥120 ms) and an oppositely directed T. I don’t see a single replaced/wide complex here—just normal QRS spikes at a steady cadence.

·         The “extra” deflections are mid-cycle, in the ST/T segment, ~200–300 ms after the prior R. That’s too late to be the onset of ventricular depolarization. It’s the classic location for motion/electrode-contact artifact on chest belts (brief, knife-edge spikes or notches that distort the T wave).

·         RR timing doesn’t show a premature beat. With a PVC (even an interpolated one), you’d expect a shortened coupling interval to the ectopic QRS and/or a compensatory pause. Here the R–R intervals bracketing those deflections stay nearly unchanged.

·         No fusion pattern. A fusion beat would alter the QRS at that beat (intermediate morphology), not insert a narrow spike in the T wave while leaving the surrounding QRS unchanged.

So while you may well have PVCs elsewhere in longer recordings, the circled “VPC activity” in this panel (like the prior ones) is most consistent with artifact—likely strap bounce/electrode pop or myopotentials—rather than true ventricular ectopy.

If you want to verify quickly in Kubios:

1.      Measure QRS width and compare suspect beats to neighbors—if identical and <120 ms, not a PVC.

2.      Check RR before/after the event. PVC: notable shortening then lengthening (or an extra premature QRS if interpolated). Your strip: essentially unchanged.

3.      Build a median template of normal beats and cross-correlate the suspect QRS; a PVC will have a low correlation and different axis; artifact in the T wave won’t.

Practical tweaks to reduce this: snug/rewet the H10 pads (a bit of gel helps), clean contacts, rotate the strap slightly to improve the Lead-II vector, and minimize torsional chest motion. For definitive arrhythmia confirmation, a 3-electrode adhesive patch or 12-lead snapshot will settle it immediately. I give up!

Bottom line here is that the VPCs were totally missed despite multiple attempts to nudge the evidence in the right direction. Obviously, this is not extending a reassuring degree of trust. Although some of these responses are spot on, many are way off and/or misleading.

 

Some observations on ChatGPT:

Can we trust it?

Some say yes, but many sources are skeptical. I’m somewhere in between. I’ve used it for some of my endocrinology lectures, tough cases and random searches that would have been time consuming using the "big G engine" that yields 10 million hits. Most of the time it’s uncannily accurate, but as in the arrhythmia example above, not quite always ready for prime time.

Funny that this was a headline in today’s news:

On the other hand, according to my ChatGPT query, this is a summary of accuracy stats:

My advice to you is use it but verify the source or citation. It’s also a good idea to do a Google Scholar or PubMed search for the most up to date material.

As a final observation, the following is a video clip of one of the questions being answered. The point being, the steps and amount of time it spent on "thinking" about it. The speed is 10x of original: