Using detrended fluctuation analysis and fast Fourier transformation of major peaks to estimate maximal lactate steady state from electrocardiograms of exercising horses
Bibliographic record
Abstract
Objective: To compare maximal lactate steady-state (MLSS) speeds determined using a treadmill-dependent invasive reference method (RM) with 2 noninvasive methods based on heart rate variability-focused analysis of exercise ECGs. Methods: This was a randomized, blinded study using 7 fit Thoroughbreds. A standardized incremental exercise treadmill test (SET) where blood lactate concentration ([La]) was measured after every step facilitated calculation of speeds at which [La] was 1.5, 2.0, and 2.5 mmol/L. The RM required steady-state exercise (SS) at each of these speeds for 25 minutes or until [La] increased > 1 mmol/L from that after 5 minutes of exercise. The fastest speed at which a horse ran was 25 minutes at SS = MLSS. Electrocardiograms were recorded for each SET and SS, assigned randomized numbers, and distributed for analyses using (1) detrended fluctuation analysis-α1 (DFAα1), and (2) smartphone-capable 4- to 30-Hz spectral analysis to determine the speed associated with the minimum number of major peaks (MPs). Bland-Altman plots assessed agreement between methods and paired t tests compared RM MLSS speeds with those calculated from the SET and SS by DFAα1 and MPs, respectively (P < .05). Results: The RM MLSS was not different from MLSS determined by MPs from SS runs, and Bland-Altman plots revealed good agreement between these speeds but wide 95% agreement intervals. Comparisons of RM MLSS with other methods showed an approximately equal to 1-m/s bias and poor agreement. Conclusions: Major peaks but not DFAα1 provided acceptable estimates of MLSS from ECGs recorded during SS. Clinical Relevance: Major peaks may be a practically useful noninvasive exercise ECG-based field method for estimating MLSS if the variability between calculated MP and MLSS speeds can be reduced.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".