Recurrence quantification analysis of heart rate variability to detect both ventilatory thresholds

Abstract

Aims of this study were: to verify if Recurrence Quantification Analysis (RQA) of Heart Rate Variability (HRV) time series could determine both ventilatory thresholds in individuals with different fitness levels, and to assess the validity of RQA method compared to gas-exchange method (GE). The two thresholds were estimated in thirty young individuals during incremental exercise on cycle-ergometer: Heart rate (HR), Oxygen consumption (VO2) and Workload were measured by the two methods (RQA and GE). Repeated measures ANOVA was used to assess main effects of methods and methods-by-groups interaction effects for HR, VO2 and Workload at aerobic (AerT) and anaerobic (AnT) thresholds. Validity of RQA at both thresholds was assessed for HR, VO2 and Workload by Ordinary Least Products (OLP) regression, Typical Percentage Error (TE), Intraclass Correlation Coefficients (ICC) and the Bland Altman plots. No methods-by-groups interaction effects were detected for HR, VO2 and Workload at AerT and AnT. The OLP analysis showed that at both thresholds RQA and GE methods had very strong correlations (r >0.8) in all variables (HR, VO2 and Workload). Slope and intercept values always included the 1 and the 0, respectively. At AerT the TE ranged from 4.02% (5.48 bpm) to 10.47% (8.53 Watts) (HR and Workload, respectively) and in all variables ICC values were excellent (≥0.85). At AnT the TE ranged from 2.53% (3.98 bpm) to 6.64% (7.81 Watts) (HR and Workload, respectively) and in all variables ICC values were excellent (≥0.90). Therefore, RQA of HRV time series is a new valid approach to determine both ventilatory thresholds in individuals with different physical fitness levels, it can be used when gas analysis is not possible or not convenient.

Unsupervised Clustering of Heartbeat Dynamics Allows for Real Time and Personalized Improvement in Cardiovascular Fitness

“…An important parameter of RQA analysis is determinism (DET) which is an indicator of the regularity or complexity of the system dynamics. In earlier works, a correlation between high values of DET and fatigue during submaximal incremental exercise has been found [ 49 , 50 ]. We found an increasing trend for DET in every running session.…”

“…Time series were analyzed in terms of Recurrence Quantification Analysis (RQA) which can be defined as a graphical, statistical, and analytical tool [ 44 , 45 ] used by several disciplines from physiology [ 46 , 47 , 48 , 49 , 50 , 51 ] to earth science [ 52 , 53 , 54 ] and economics [ 55 , 56 , 57 ]. The RQA-based method employed in the analysis of HR time series is widely explained in earlier papers [ 49 , 50 ]. To perform RQA computation we used a software written in Python [ 58 ].…”

A Systematic Review and Meta-Analysis on the Association and Differences between Aerobic Threshold and Point of Optimal Fat Oxidation

“…The current results have wide practical implications. Development of affordable and unconventional field test strategies, such as methods using heart rate variability [ 58 ], perceived exertion [ 59 ], and respiratory frequency [ 60 ], could allow the identification of the AerT with relatively good accuracy, simplicity, and velocity. From a physiological perspective, AerT can be considered as a “biomarker” for FAT max identification (vice versa), which in turn could replace costly tests aiming to detect mentioned indices.…”

Physical Education on the Beach: An Alternative Way to Improve Primary School Children’s Skill- and Health-Related Outcomes during the COVID-19 Pandemic

“…In the present study, the equation of Matsuzaka et al [ 30 ] was used to predict VO 2peak from the PACER scores. Recently, a new approach was implemented in adults to monitor their fitness status by detecting aerobic threshold from HR variability [ 57 , 58 ]. Further research could be conducted to investigate this method as a complementary criterion for testing VO 2peak in children and youth.…”