An investigation into the relationship between heart rate variability and the ventilatory threshold in healthy moderately trained males

Deep learning-based dynamic ventilatory threshold estimation from electrocardiograms

BACKGROUND AND OBJECTIVE

The ventilatory threshold (VT) marks the transition from aerobic to anaerobic metabolism and is used to assess cardiorespiratory endurance. A conventional way to assess VT is cardiopulmonary exercise testing, which requires a gas analyzer. Another method for measuring VT involves calculating the heart rate variability (HRV) from an electrocardiogram (ECG) by computing the variability of heartbeats. However, the HRV method has some limitations. ECGs should be recorded for at least 5 minutes to calculate the HRV, and the result may depend on the utilized ECG preprocessing algorithms.

METHODS

To overcome these problems, we developed a deep learning-based model consisting of long short-term memory (LSTM) and convolutional neural network (CNN) for a lead II ECG. Variables reflecting subjects' physical characteristics, as well as ECG signals, were input into the model to estimate VT. We applied joint optimization to the CNN layers to generate an informative latent space, which was fed to the LSTM layers. The model was trained and evaluated on two datasets, one from the Bruce protocol and the other from a protocol including multiple tasks (MT).

RESULTS

Acceptable performances (mean and 95% CI) were obtained on the datasets from the Bruce protocol (-0.28[-1.91,1.34] ml/min/kg) and the MT protocol (0.07[-3.14,3.28] ml/min/kg) regarding the differences between the predictions and labels. The coefficient of determination, Pearson correlation coefficient, and root mean square error were 0.84, 0.93, and 0.868 for the Bruce protocol and 0.73, 0.97, and 3.373 for the MT protocol, respectively.

CONCLUSIONS

The results indicated that it is possible for the proposed model to simultaneously assess VT with the inputs of successive ECGs. In addition, from ablation studies concerning the physical variables and the joint optimization process, it was demonstrated that their use could boost the VT assessment performance of the model. The proposed model enables dynamic VT estimation with ECGs, which could help with managing cardiorespiratory fitness in daily life and cardiovascular rehabilitation in patients.

Heart Rate Variability-Derived Thresholds for Exercise Intensity Prescription in Endurance Sports: A Systematic Review of Interrelations and Agreement with Different Ventilatory and Blood Lactate Thresholds

BACKGROUND

Exercise intensities are prescribed using specific intensity zones (moderate, heavy, and severe) determined by a 'lower' and a 'higher' threshold. Typically, ventilatory (VT) or blood lactate thresholds (LT), and critical power/speed concepts (CP/CS) are used. Various heart rate variability-derived thresholds (HRVTs) using different HRV indices may constitute applicable alternatives, but a systematic review of the proximity of HRVTs to established threshold concepts is lacking.

OBJECTIVE

This systematic review aims to provide an overview of studies that determined HRVTs during endurance exercise in healthy adults in comparison with a reference VT and/or LT concept.

METHODS

A systematic literature search for studies determining HRVTs in healthy individuals during endurance exercise and comparing them with VTs or LTs was conducted in Scopus, PubMed and Web of Science (until January 2022). Studies claiming to describe similar physiological boundaries to delineate moderate from heavy (HRVTlow vs. VTlow and/or LTlow), and heavy from severe intensity zone (HRVThigh vs. VThigh and/or LThigh) were grouped and their results synthesized.

RESULTS

Twenty-seven included studies (461 participants) showed a mean difference in relative HR between HRVTlow and VTlow of - 0.6%bpm in weighted means and 0.02%bpm between HRVTlow and LTlow. Bias between HR at HRVTlow and VTlow was 1 bpm (limits of agreement (LoA): - 10.9 to 12.8 bpm) and 2.7 bpm (LoA: - 20.4 to 25.8 bpm) between HRVTlow and LTlow. Mean difference in HR between HRVThigh and VThigh was 0.3%bpm in weighted means and 2.9%bpm between HRVThigh and LThigh while bias between HR at HRVThigh and VThigh was - 4 bpm (LoA: - 17.9 to 9.9 bpm) and 2.5 bpm (LoA: - 12.1 to 17.1 bpm) between HRVThigh and LThigh.

CONCLUSION

HRVTlow seems to be a promising approach for the determination of a 'lower' threshold comparable to VTlow and potentially for HRVThigh compared to VThigh, although the latter needs further empirical evaluation. LoA for both intensity zone boundaries indicates bias of HRVTs on an individual level. Taken together, HRVTs can be a promising alternative for prescribing exercise intensity in healthy, male athletes undertaking endurance activities but due to the heterogeneity of study design, threshold concepts, standardization, and lack of female participants, further research is necessary to draw more robust and nuanced conclusions.

Reliability of the Heart Rate Variability Threshold During Treadmill Exercise

The heart rate variability threshold (HRVT) is a clinical parameter used to gain insight into autonomic balance. Prior validation of the HRVT has been with cycle ergometry, with no studies examining the viability of treadmill exercise. The purpose of this study was to examine the reliability of the HRVT during treadmill exercise, and to compare the HRVT to the ventilatory threshold (VT). Ten healthy, college‐aged males completed two maximal graded exercise tests on a treadmill. A Polar RS800CX watch was used for heart rate and HRVT data. The HRVT was determined from three HRV variables including the root mean square of successive differences of continuous R−R intervals (RMSSD), the standard deviation of normal R−R intervals (SDNN) and the standard deviation of instantaneous beat intervals (SD1). A metabolic cart was utilized to determine the VT. Results showed no difference between the HRVT (2.4 ± 0.6 and 2.2 ± 0.3 for RMSSD, 2.8 ± 0.5 and 2.7 ± 0.5 for SDNN and 2.4 ± 0.6 and 2.3 ± 0.6 for SD1) or the VT (3.0 ± 0.3 and 3.1 ± 0.3) between trials. When compared to the VT, averaged HRVT values for RMSSD (2.3 ± 0.3) and SD1 (2.3 ± 0.5) were lower than averaged VT (2.8 ± 0.4, p < 0.05). The averaged HRVT from SDNN (2.8 ± 0.5) did not differ from the VT. These results suggest that treadmill is a viable mode for HRVT determination, and that HRVT determined by SDNN may be a better comparison to the VT.

Constant power threshold—predicting maximal lactate steady state in recreational cyclists

Introduction

Prolonged time trials proved capable of precisely estimating anaerobic threshold. However, time trial studies in recreational cyclists are missing. The aim of the present study was to evaluate accuracy and viability of constant power threshold, which is the highest power output constantly maintainable over time, for estimating maximal lactate steady state in recreational athletes.

Methods

A total of 25 recreational athletes participated in the study of whom 22 (11 female, 11 male) conducted all constant load time trials required for determining constant power threshold 30 min and 45 min, which is the highest power output constantly maintainable over 30 min and 45 min, respectively. Maximal lactate steady state was assessed subsequently from blood samples taken every 5 min during the time trials.

Results

Constant power threshold over 45 min (175.5 ± 49.6 W) almost matched power output at maximal lactate steady state (176.4 ± 50.5 W), whereas constant power threshold over 30 min (181.4 ± 51.4 W) was marginally higher (P = 0.007, d = 0.74). Interrelations between maximal lactate steady state and constant power threshold 30 min and constant power threshold 45 min were very close (R2 = 0.99, SEE = 8.9 W, Percentage SEE (%SEE) = 5.1%, P < 0.001 and R2 = 0.99, SEE = 10.0 W, %SEE = 5.7%, P < 0.001, respectively).

Conclusions

Determination of constant power threshold is a straining but viable and precise alternative for recreational cyclists to estimate power output at maximal lactate steady state and thus maximal sustainable oxidative metabolic rate.

Validation of a smart shirt for heart rate variability measurements at rest and during exercise

Heart rate variability (HRV) monitoring is a promising option to estimate the autonomic nervous system regulation responding to exercise. Textiles with embedded sensors recording heartbeat intervals are a simple tool for data collection. So-called smart shirts offer comfort for a daily use and are managed easily. Their measurement accuracy for HRV calculation at rest is promising but remains questionable during exercise. Therefore, the present study validated the Ambiotex smart shirt using HRV indices (RMSSD, rel. HF power and rel. LF power) during exercise. Eighty-three healthy participants (31 ± 6 years; 39 females, 44 males) completed an incremental exercise test on a bicycle ergometer wearing the smart shirt and an electrocardiogram simultaneously. We compared HRV indices of segments at rest (5 min), at warm-up (3 min) and twice at the exercise test (each 5 min). At rest and at warm-up, we observed excellent linear relationship (r > 0.96; R² > 0.94), excellent relative reliability (ICC ≥ 0.98; α ≥ 0.98) and acceptable agreement (bias < 10%). During the exercise test, measurement accuracy declined with increasing intensity but remained high (> 0.8), although results for partial HRV indices were insufficient. In addition, percentage bias was unacceptable during exercise test. However, the findings support the validity of the smart shirt for measuring HRV especially at rest and at warm-up. We suggest using the smart shirt for monitoring HRV indices on a daily basis but caution should be taken in the interpretation of HRV indices obtained during moderate to vigorous exercise intensities. This article is protected by copyright. All rights reserved.

Multiannual, Intensive Strength-Endurance Training Modulates the Activity of the Cardiovascular and Autonomic Nervous System among Rowers of the International Level

Introduction

Professional athlete training is significantly different from recreational physical activity, and sustained, repetitive exposure to over-strenuous and intensive training may result in critical changes of most systems and organs in a sportsman's body.

Aim

The assessment of the influence of multiannual strength-endurance training on the autonomic nervous system (ANS) and cardiovascular system (CVS) among the rowers of Polish national team.

Materials and Methods

20 rowers, aged 20–30, seniors of Polish national team were qualified into the study. The functional examination of ANS was conducted by means of Task Force® Monitor system. The assessed parameters included hemodynamic parameters, heart rate, and blood pressure variability and reflexes sensitivity of baroreceptors. In order to examine and compare the reaction of autonomic nervous system the subjects underwent a tilt test.

Results

In the study group, significantly higher levels of sBP (129.3  ±  12.2 vs 118.3 ± 8.4, p = 0.0030), SI (59.9 ± 8.8 vs 41.2 ± 6.8, p > 0.001), CI (3.2 ± 0.5 vs 2.4 ± 0.4, p > 0.001), and significantly lower levels of HR (54.2 ± 5.3 vs 60.1 ± 5.7, p = 0.0034) and TPRI (2333.3 ± 389.9 vs 2950.2 ± 604.2, p = 0.0012) compared to the control group, were found. After the tilt test the levels of HR (p = 0.0005) and TPRI (p = 0.0128) were significantly higher but SI (p > 0.001) and CI (p = 0.0006) were significantly lower in the study group compared to the control.

Conclusions

Multiannual strength-endurance training connected with rowing activities substantially modulates the activity of cardiovascular and autonomic nervous system, influences the volumetric workload of the heart and structural changes, and increases the sensitivity of reflexes of arterial baroreceptors.

Hypothalamic PVN contributes to acute intermittent hypoxia-induced sympathetic but not phrenic long-term facilitation

Blackburn MB, Andrade MA, Toney GM. Hypothalamic PVN contributes to acute intermittent hypoxia-induced sympathetic but not phrenic long-term facilitation. J Appl Physiol 124: 1233-1243, 2018. First published December 19, 2017; doi: 10.1152/japplphysiol.00743.2017 .- Acute intermittent hypoxia (AIH) repetitively activates the arterial chemoreflex and triggers a progressive increase of sympathetic nerve activity (SNA) and phrenic nerve activity (PNA) referred to as sympathetic and phrenic long-term facilitation (S-LTF and P-LTF), respectively. Neurons of the hypothalamic paraventricular nucleus (PVN) participate in the arterial chemoreflex, but their contribution to AIH-induced LTF is unknown. To determine this, anesthetized rats were vagotomized and exposed to 10 cycles of AIH, each consisting of ventilation for 3 min with 100% O₂ followed by 3 min with 15% O₂. Before AIH, rats received bilateral PVN injections of artificial cerebrospinal fluid (aCSF; vehicle) or the GABA-A receptor agonist muscimol (100 pmol in 50 nl) to inhibit neuronal activity. Thirty minutes after completing the AIH protocol, during which rats were continuously ventilated with 100% O₂, S-LTF and P-LTF were quantified from recordings of integrated splanchnic SNA and PNA, respectively. PVN muscimol attenuated increases of SNA during hypoxic episodes occurring in later cycles (6-10) of AIH ( P < 0.03) and attenuated post-AIH S-LTF ( P < 0.001). Muscimol, however, did not consistently affect peak PNA responses during hypoxic episodes and did not alter AIH-induced P-LTF. These findings indicate that PVN neuronal activity contributes to sympathetic responses during AIH and to subsequent generation of S-LTF. NEW & NOTEWORTHY Neural circuits mediating acute intermittent hypoxia (AIH)-induced sympathetic and phrenic long-term facilitation (LTF) have not been fully elucidated. We found that paraventricular nucleus (PVN) inhibition attenuated sympathetic activation during episodes of AIH and reduced post-AIH sympathetic LTF. Neither phrenic burst patterning nor the magnitude of AIH-induced phrenic LTF was affected. Findings indicate that PVN neurons contribute to AIH-induced sympathetic LTF. Defining mechanisms of sympathetic LTF could improve strategies to reduce sympathetic activity in cardiovascular and metabolic diseases.