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.

The Autonomic Imbalance of Myocardial Ischemia during Exercise Stress Testing: Insight from Short-Term Heart Rate Variability Analysis

“…The complexity of HRV is moderated by the dynamic interaction between the sympathetic and parasympathetic nervous systems [ 16 ]. Compared with HRR, the complexity of HRV is not yet fully understood in supposed correspondence with different HRV powers [ 28 ].…”

“…SD1 is the fast beat-to-beat variability in the RR intervals, while SD2 describes the longer-term variability. SD1 reflects mainly the parasympathetic input to the heart, while SD2 reflects the sympathetic and parasympathetic contributions to the heart [ 7 , 16 ].…”

Exploring the mechanisms underlying stroke volume variability reduction in a murine model of heart failure with reduced ejection fraction

“…SD2 is also known as the long-term variability, and it reflects the magnitude of the fluctuations of the system at a longer time scale. In the case of heart rate variability, SD2 is related to the sympathetic nervous system activity [ 22 ].…”

Metabolic flexibility and resting autonomic function in active menopausal women

“…The detrended uctuation analysis (DFA) algorithm was employed to explore correlations between RR intervals at various time scales (Blasco-Lafarga et al, 2017). This method is useful for assessing long-term autocorrelation in non-stationary time series (Zimatore et al, 2022). It provides insights into cardiac system uctuations across multiple time scales, with lower self-similarity indicating a more randomly structured, less adaptive, and less exible system (Pham et al, 2021).…”