Detecting Metabolic Thresholds from Nonlinear Analysis of Heart Rate Time Series: A Review

Abstract

Heart rate time series are widely used to characterize physiological states and athletic performance. Among the main indicators of metabolic and physiological states, the detection of metabolic thresholds is an important tool in establishing training protocols in both sport and clinical fields. This paper reviews the most common methods, applied to heart rate (HR) time series, aiming to detect metabolic thresholds. These methodologies have been largely used to assess energy metabolism and to identify the appropriate intensity of physical exercise which can reduce body weight and improve physical fitness. Specifically, we focused on the main nonlinear signal evaluation methods using HR to identify metabolic thresholds with the purpose of identifying a method which can represent a useful tool for the real-time settings of wearable devices in sport activities. While the advantages and disadvantages of each method, and the possible applications, are presented, this review confirms that the nonlinear analysis of HR time series represents a solid, robust and noninvasive approach to assess metabolic thresholds.

Keywords: Poincaré plot; heart rate variability; metabolic threshold; nonlinear dynamic; recurrence quantification analysis; sport; wearable devices.

Figure A1

Top panel: the same picture as Figure 5. Bottom panel: the percentage of recurrence points which form diagonal lines (DET) and the percentage of recurrence points which form vertical lines (LAM) epoch by epoch vs. time (min:sec) of detrended RR interval of Figure 5 (light blue line), recorded as a breath-by-breath, from a CPET device (Cosmed, Rome, Italy), in the sub-maximal test where the workload changed every 60 s. The red line highlights the statistically relevant minimum that corresponds to the AerT, and the green line corresponds to the AnT where the percent of determinism reaches saturation (see Appendix A.4).

Figure 1

Heart rate variation (in beats per minute—bpm) of a healthy female subject during an incremental exercise (for more details see Appendix A.4).

Figure 2

Spectral analysis of heart rate (HR) of a healthy female subject (the same as shown in Figure 1, for more details see Appendix A.4).

Figure 3

(a) Poincaré plot of RR time series from a healthy subject at rest; (b) Poincaré plot of the same time series as in Figure 1 (for more details see Appendix A.4).

Figure 4

Detrended Fluctuation Analysis (DFA) of RR time series from a healthy subject (the same as shown in Figure 1, for more details see Appendix A.4).

Figure 5

Unthresholded recurrence plot of the RR time series (ms) (light blue line) recorded as a breath-by-breath, from a cardiopulmonary exercise test (CPET) device (Cosmed, Rome, Italy). The red and green lines show the change of pattern at the first and second threshold, respectively. On the horizontal and vertical axes, the j-th and i-th indices are reported, respectively. (For more details see in Appendix A.3 and Appendix A.4).

Figure 6

How the threshold (lowest red point) is determined by the Dmax method: it is estimated by the longest perpendicular distance between SD1 (predicted by a third order polynomial function over actual value) from the linear regression calculated with the first and last values of the curve. The speed (km/h) corresponds to the treadmill velocity.

Figure 7

From the subject to the method: a presentation of the principal limitations in the HR data processing. In the insets: ECG, Experimental set: treadmill and facemask, Quark RMR-CPET Cosmed, Rome, Italy.