Real‐Time Analysis of the Heart Rate Variability During Incremental Exercise for the Detection of the Ventilatory Threshold

Abstract

BackgroundIt has never been possible to immediately evaluate heart rate variability (HRV) during exercise. We aimed to visualize the real‐time changes in the power spectrum of HRV during exercise and to investigate its relationship to the ventilatory threshold (VT).Methods and ResultsThirty healthy subjects (29.1±5.7 years of age) and 35 consecutive patients (59.0±13.2 years of age) with myocardial infarctions underwent cardiopulmonary exercise tests with an RAMP protocol ergometer. The HRV was continuously assessed with power spectral analyses using the maximum entropy method and projected on a screen without delay. During exercise, a significant decrease in the high frequency (HF) was followed by a drastic shift in the power spectrum of the HRV with a periodic augmentation in the low frequency/HF (L/H) and steady low HF. When the HRV threshold (HRVT) was defined as conversion from a predominant high frequency (HF) to a predominant low frequency/HF (L/H), the VO 2 at the HRVT (HRVT‐VO 2) was substantially correlated with the VO 2 at the lactate threshold and VT) in the healthy subjects (r=0.853 and 0.921, respectively). The mean difference between each threshold (0.65 mL/kg per minute for lactate threshold and HRVT, 0.53 mL/kg per minute for VT and HRVT) was nonsignificant (P>0.05). Furthermore, the HRVT‐VO 2 was also correlated with the VT‐VO 2 in these myocardial infarction patients (r=0.867), and the mean difference was −0.72 mL/kg per minute and was nonsignificant (P>0.05).ConclusionsA HRV analysis with our method enabled real‐time visualization of the changes in the power spectrum during exercise. This can provide additional information for detecting the VT.

Association between Mean Heart Rate and Recurrence Quantification Analysis of Heart Rate Variability in End-Stage Renal Disease

“…Several approaches involving the concept of entropy have been applied to the analysis of time series (particularly for HRV time series). Methods such as maximum entropy [ 44 ], sample entropy [ 45 ], approximate entropy [ 46 ], fuzzy measure entropy [ 47 ], multiscale entropy [ 48 ], and renormalized entropy [ 49 ] have proven to differentiate pathologies and study physiological processes. Since every entropy method may have different assumptions, a systematic review with a comprehensive comparison of these methods and their application is warranted in further investigations.…”

Feasibility of the deep learning method for estimating the ventilatory threshold with electrocardiography data

“…Alternative methods are needed to facilitate the estimation of aerobic exercise thresholds and expand exercise therapy to the outpatient setting. We have previously demonstrated that a real-time evaluation of the HR variability (HRV) with single-lead ECG during CPX could be helpful for detecting the aerobic exercise threshold 25 . The study targeted patients who had myocardial infarction and sinus rhythm on ECG but not arrhythmia, such as AF, because the HRV analysis is not applicable in patients with irregular R–R intervals on ECG.…”

Handrail support interference in cardiac autonomic modulation adjustments in young adults during maximal exercise testing

“…This response is associated with progressive inhibition of the parasympathetic pathway that occurs until it reaches the ventilatory threshold, when it disappears 16,[22][23][24][25][26][27] . Shiraishi et al 16 and Karapetian et al 27 verified that there is a strong relation between the ventilatory threshold and the change that occurs in HF of the HRV in the transition from aerobic to anaerobic metabolism.…”

“…these findings, Shiraishi et al 16 recently presented, in real time, the HRV dynamics during an incremental ETT. They observed that the ventilatory threshold corresponds to the dynamics of the high frequency (HF) response in healthy young adults and in individuals who suffered myocardial infarction.…”