Effects of exercise on heart rate variability by time-domain, frequency-domain and non-linear analyses in equine athletes

Data source and heart variability analysis

Heart rate data of horses were obtained from a publicly available dataset published online with the study protocol previously described³. Briefly, healthy Thoroughbred horses in race training presented for workups at an equine hospital were screened. This yielded a total of seven horses with electrocardiographic recordings for 10 to 18 minutes. HRV analysis was performed using Kubios HRV Standard software (Version 3.0.2). The following time-domain measures were obtained: 1) mean RR interval; 2) standard deviation (SD) of RR intervals; 3) coefficient of variation (CoV) for RR intervals, 4) root mean square (RMSSD) of successive differences of RR intervals; 5) NN50, number of successive RR interval pairs that differ more than 50 ms; 6) pNN50, relative number of successive RR interval pairs that differ more than 50 ms; 7) HRV triangular index, the integral of the RR interval histogram divided by the height of the histogram; 8) Triangular interpolation of normal-to-normal intervals (TINN). 9) mean HR; 10) SD of HR; 11) CoV for HR; 12) minimum HR; 13) maximum HR.

Frequency-domain analysis was performed using the Fast Fourier Transform method⁴ with sampling frequency set at 8 Hz. The power in the repolarization spectrum between 0.04 and 0.4 Hz was defined as total power (TP). The power in the heart rate spectrum was divided into three different frequency bands: very low frequency power (VLF, 0 to 0.04 Hz), low frequency power (LF, 0.04 to 0.15 Hz) and high frequency power (HF, 0.15 to 0.4 Hz).

Non-linear properties of HRV were studied as follow. Poincaré plots are graphical representations of the correlation between successive RR intervals, in which RR_n+1 is plotted against RR_n. From this plot, the SD of the points perpendicular to the line-of-identity (SD1) describing short-term variability, and the SD of the points along the line-of-identity (SD2) describing the long-term variability, can be determined. The SD2/SD1 ratio is a measure of long-term variability relative to the short-term variability. The approximate entropy provides a measure of the irregularity of the signal. It is computed as follows:

Firstly, a set of length m vectors u_j is formed:

u_j = (RR_j ; RR_j+1,…, RR_j+m-1); j = 1; 2; …N – m + 1

where m is the embedding dimension and N is the number of measured RR intervals. The distance between these vectors is defined as the maximum absolute difference between the corresponding elements:

d(u_j, u_k) = max {|RR_j+n – RR_k+n| | n=0, …, m-1}

for each u_j the relative number of vectors u_k for which d(u_j, u_k) ≤ r is calculated. This index is denoted with Cmj (r) and can be written in the form

$C_j^m(r)=\frac{nbrof\left\{u_k|d\left(u_j,u_k\right)\le r\right\}}{N-m+1}\forall k$

Taking the natural logarithms gives:

${\mathrm{\Phi }}^m(r)=\frac{1}{N-m+1}{\displaystyle \sum _{j=1}^{N-m+1}\ln C_j^m(r).}$

The approximate entropy is then defined as:

$ApEn\left(m,r,N\right)={\mathrm{\Phi }}^m(r)-{\mathrm{\Phi }}^{m+1}(r)$

Lower approximate entropy values reflect a more regular signal, whereas higher values reflect a more irregular signal.

The sample entropy also provides a measure of signal irregularity but is less susceptible to bias compared to approximate entropy. This is given by:

$C_j^m(r)=\frac{nbrof\left\{u_k|d\left(u_j,u_k\right)\le r\right\}}{N-m}\forall k\ne j$

Averaging then yields:

$C^m(r)=\frac{1}{N-m+1}{\displaystyle \sum _{j=1}^{N-m+1}{C}_j^m(r)}$

The sample entropy is then given by:

$\text{SampEn}\text{(}m,r,N\text{)}=\ln (\frac{C^m(r)}{C^{m+1}(r)})$

Finally, detrended fluctuation analysis (DFA) was performed to determine long-range correlations in non-stationary physiological time series⁵, yielding both short-term fluctuation (α1) and long-term fluctuation (α2) slopes. The point at which the slopes α1 and α2 is the crossover point.

Statistical analysis

Statistical analyses were conducted using Origin Pro 2017. All values were expressed as mean ± standard deviation (SD). Numerical data were compared by one-way analysis of variance (ANOVA). P<0.05 was considered statistically significant and was denoted by * in the figures.

Heart rate variability determined using time-domain and frequency-domain methods

Representative time series data for RR intervals at rest and during exercise from a single horse are shown in Figure 1A and Figure 1B, respectively, with their frequency distributions shown in Figure 1C and Figure 1D. Their corresponding heart rate time series are shown in Figure 2A and Figure 2B, and frequency distributions in Figure 2C and Figure 2D. Under resting conditions, the mean RR interval was 1392±224 ms, decreasing to 541±84 with exercise (n=7 horses) (Figure 3A). The mean standard deviation (SD) was 65±43 ms (Figure 3B) and coefficient of variation (CoV) was 5±2% (Figure 3C), which decreased to 8±4 ms and 1±1%, respectively, after exercise (ANOVA, P<0.05). Similarly, the root mean square of successive RR interval differences (RMSSD) decreased from 89±92 to 6±4 ms (Figure 3D). The number of interval differences of successive NN intervals greater than 50 ms (NN50) and the proportion derived by dividing NN50 by the total number of NN intervals (pNN50) were decreased from 85±55 to 0±1 ms and from 21±12 to 0±1%, respectively (ANOVA, P<0.05). The HRV triangular index, which is integral of the RR interval histogram divided by the height of the histogram, also decreased from 8.37±2.87 to 2.03±0.45 (ANOVA, P<0.05), as was the triangular interpolation of normal-to-normal intervals (TINN), the baseline width of the RR interval histogram (445± 240 to 40±26 ms; ANOVA, P<0.05). These corresponded to a mean heart rate of 44±8 bpm (Figure 4A), SD was 7±2 bpm (Figure 4B) and CoV (Figure 4C) of 16±4%. With exercise, HR increased to 113±17 bpm, whereas SD, CoV and RMSSD decreased to 4±2 bpm, 3±2% and 6±4 ms, respectively. Finally, the minimum and maximum heart rates (Figure 4D and 4E) at rest were 34±5 and 79±16 bpm, respectively, increasing to 101±16 and 129±15 bpm.

Figure 1.

Representative time series data for RR intervals at rest (A) and during exercise (B) and the corresponding histograms (C and D) from a single horse.

Figure 2.

Representative time series data for heart rates at rest (A) and during exercise (B) and the corresponding histograms (C and D) from a single horse.

Figure 3.

Time-domain analysis (n=7 horses) yielding mean RR intervals (A), standard deviation (SD) of RR intervals (B), coefficient of variation (CoV) given by SD/mean x 100% (C), root mean square of successive RR interval differences (RMSSD) (D), number of interval differences of successive NN intervals greater than 50 ms (NN50) (E), proportion derived by dividing NN50 by the total number of NN intervals (pNN50) (F), heart rate variability triangular index (HRVTI), the integral of the RR interval histogram divided by the height of the histogram (G) and triangular interpolation of normal-to-normal intervals (TINN), the baseline width of the RR interval histogram (H).

Figure 4.

Time-domain analysis yielding mean heart rate (HR) (A), standard deviation (SD) of HR (B), coefficient of variation (CoV) given by standard deviation (SD)/mean x 100% (C), minimum HR (D) and maximum HR (E).

Next, the Fast Fourier Transform method was used for frequency-domain analyses. An example of the power spectrum plot against frequency before and after exercise is shown in Figure 5A and 5B, respectively. Strikingly, the peaks for very low-, low- and high-frequency were not altered by exercise (0.04 ± 0.00 vs. 0.04 ± 0.00 Hz; 0.10 ± 0.03 vs. 0.09±0.03 Hz; 0.19 ± 0.03 vs. 0.18±0.03 Hz, respectively) (Figures 5C to E). Similarly, their percentage powers remained unchanged (2±1 vs. 4±5%; 59±9 vs. 64±20%; 39±10 vs. 32±19%, respectively, P>0.05) (Figures 5F to 5H). By contrast, very low-frequency, low-frequency, and high-frequency powers were significantly reduced from 29±17 to 2±5 ms², 1138±372 to 22±22 ms² and 860±564 to 9±6 ms², respectively (P<0.05) (Figures 6A to 6C), as was the total power (in logarithms) from 7.52±0.52 to 3.25±0.73 (P<0.05) (Figures 6D).

Figure 5.

Examples of frequency spectra using the Fast Fourier Transform method for RR time series obtained before (A) and after (B) exercise. Peaks for very low-frequency (VLF) (C), low-frequency (LF) (D) and high-frequency (HF) (E) and their percentage powers (F to H).

Figure 6.

Very low-frequency (VLF) (C), low-frequency (LF) (D) and high-frequency (HF) (E) powers and total power in logarithms (D).

Heart rate variability determined using non-linear methods

Poincaré plots expressing RR_n+1 as a function of RR_n were constructed, with typical examples from a single horse before and after exercise shown in Figure 7A and 7B. In all of the hearts studied, circular shapes of the data points were observed. The SD perpendicular to the line-of-identity (SD1) and SD along the line-of-identity (SD2) are shown in Figure 7C and 7D, respectively. The mean SD1 and SD2 were 63±65 and 62±17, respectively, corresponding to a SD2/SD1 ratio of 1.33±0.45 (Figure 7E). After exercise, SD1 and SD2 decreased significantly to 5±3 and 9±5, respectively, which corresponded to increased SD2/SD1 ratio to 2.19±0.72 (P<0.05). Moreover, approximate and sample entropy took values of 0.97±0.23 (Figure 7F) and 1.14±0.43 (Figure 7G), respectively. These values were not altered after exercise (0.82±0.22 and 1.37±0.49, respectively; P>0.05). Detrended fluctuation analysis plotting the detrended fluctuations F(n) as a function of n in a log-log scale was performed for the RR intervals (Figure 8A and 8B). This revealed short- (α1) and long-term (α2) fluctuation slopes of 0.76±0.27 (Figure 8C) and 0.16±0.11 (Figure 8D) before exercise. After exercise, α1 was not significantly different (1.18±0.55; P>0.05) but α2 was significantly increased to 0.50±0.16 (P<0.05).

Figure 7.

Representative Poincaré plots of RR_n+1 against RR_n before (A) or after exercise (B) from a single horse. Summary data (n=7) for standard deviation (SD) along the line-of-identity (SD1) (C) and SD perpendicular to the line-of-identity (SD2) (D), and the SD2/SD1 ratio (E), approximate entropy (ApEn) (F) and sample entropy (Samp En) (G).

Figure 8.

Detrended fluctuation analysis (DFA) plots expressing detrended fluctuations F(n) as a function of n in a log-log scale before (A) and after exercise (B), yielding short-term (C) and long-term (D) fluctuation slopes (α1 and α2, respectively).

Underlying data

The dataset used in this analysis is available as part of Li et al. (2018)³

PLoS One: S1 Dataset. Excel file of dataset of electrocardiographic intervals in all horses.

https://doi.org/10.1371/journal.pone.0194008.s001³

License: CC BY 4.0 Attribution