Using detrended fluctuation analysis and fast Fourier transformation of major peaks to estimate maximal lactate steady state from electrocardiograms of exercising horses.

Overview

• This study evaluated two noninvasive heart rate variability-based methods for estimating the maximal lactate steady state (MLSS) speed in exercising horses using electrocardiogram (ECG) data.
• The methods were compared to a traditional invasive reference method based on blood lactate measurements during treadmill exercise.

Background and Objective

• MLSS is a key physiological parameter indicating the highest exercise intensity at which lactate production and clearance are balanced, used to define endurance capacity in horses.
• The traditional method to determine MLSS involves invasive blood lactate concentration ([La]) measurements during steady-state treadmill exercise at different speeds.
• This study sought to validate noninvasive methods based on heart rate variability analyses from ECG recordings as alternatives to invasive blood sampling.

Study Design and Methods

• Subjects: Seven fit Thoroughbred horses participated in the study.
• Exercise Protocol:
  • Standardized incremental treadmill exercise tests (SET) were performed where speeds increased stepwise.
  • Blood lactate concentrations were measured after each step to find speeds corresponding to specific lactate levels (1.5, 2.0, 2.5 mmol/L).
  • Steady-state (SS) exercise runs at these speeds lasted up to 25 minutes or until lactate rose by more than 1 mmol/L after 5 minutes, to define the MLSS.
• Data Collection and Analysis:
  • ECGs were recorded during both SET and SS exercises.
  • ECG data were anonymized and randomized for analysis.
  • Two noninvasive analysis methods were applied:
    • Detrended Fluctuation Analysis α1 (DFAα1), reflecting fractal scaling of heart rate variability.
    • Spectral analysis to identify major frequency peaks (MPs) in the 4–30 Hz range, detectable via smartphone-capable algorithms.
  • Speeds associated with MLSS calculated by these methods were compared to the reference method (RM) speeds using:
    • Bland-Altman plots to assess agreement between methods.
    • Paired t tests to compare mean MLSS speeds.

Key Findings

• The MLSS speeds determined by the reference method (blood lactate) and the MP method (from steady-state exercise ECGs) showed no significant difference.
• Bland-Altman analysis revealed good agreement but relatively wide 95% limits of agreement between RM and MP methods.
• The DFAα1 method did not provide accurate MLSS speed estimates compared to the RM, showing roughly a 1 m/s bias and poor agreement.
• MLSS estimates derived from ECG during incremental SET showed greater discrepancy compared to those derived from SS runs.

Conclusions and Implications

• The spectral analysis of major frequency peaks in exercise ECGs recorded during steady-state runs can be a reliable noninvasive alternative to invasively measured MLSS in horses.
• This method could support practical field assessments of equine fitness without the need for blood sampling or specialized lab equipment.
• However, the variability in MLSS estimates using MP requires further refinement to improve precision and reduce the range of agreement.
• The DFAα1 heart rate variability parameter was not effective for MLSS estimation in this context.
• Overall, spectral analysis of major peaks presents a promising smartphone-compatible tool for noninvasive, real-time monitoring of equine exercise physiology.