Correlation properties and respiratory frequency of ECG-derived heart rate variability during multiple race-pace running intervals in female and male long-distance runners

Aim was to evaluate alterations of the non-linear short-term scaling exponent alpha1 of detrended fluctuation analysis (DFAa1) of heart rate (HR) variability (HRV) as a sensitive marker for assessing global physiological demands during multiple running intervals. As a secondary analysis, agreement of ECG-derived respiratory frequency (EDR) compared to respiratory frequency (RF) derived from the metabolic cart was evaluated with the same chest belt device. Fifteen trained female and male long-distance runners completed four running bouts over 5 min on a treadmill at marathon pace. During the last 3 min of each bout gas exchange data and a single-channel ECG for the determination of HR, DFAa1 of HRV, EDR and RF were analyzed. Additionally, blood lactate concentration (BLC) was determined and rating of perceived exertion (RPE) was requested. DFAa1, oxygen consumption, BLC, and RPE showed stable behaviors comparing the running intervals. Only HR (p < 0.001, d = 0.17) and RF (p = 0.012, d = 0.20) indicated slight increases with small effect sizes. In addition, results point towards inter-individual differences in all internal load metrics. The comparison of EDR with RF during running revealed high correlations (r = 0.80, p < 0.001, ICC3,1 = 0.87) and low mean differences (1.8 ± 4.4 breaths/min), but rather large limits of agreement with 10.4 to -6.8 breaths/min. Results show the necessity of EDR methodology improvement before being used in a wide range of individuals and sports applications. Relationship of DFAa1 to other internal load metrics, including RF, in quasi-steady-state conditions bears the potential for further evaluation of exercise prescription and may enlighten decoupling mechanisms during prolonged exercise bouts.

Spectral Analysis of Heart Rate Variability in Time-Varying Conditions and in the Presence of Confounding Factors

The tools for spectrally analyzing heart rate variability (HRV) has in recent years grown considerably, with emphasis on the handling of time-varying conditions and confounding factors. Time-frequency analysis holds since long an important position in HRV analysis, however, this technique cannot alone handle a mean heart rate or a respiratory frequency which vary over time. Overlapping frequency bands represents another critical condition which needs to be dealt with to produce accurate spectral measurements. The present survey offers a comprehensive account of techniques designed to handle such conditions and factors by providing a brief description of the main principles of the different methods. Several methods derive from a mathematical/statistical model, suggesting that the model can be used to simulate data used for performance evaluation. The inclusion of a respiratory signal, whether measured or derived, is another feature of many recent methods, e.g., used to guide the decomposition of the HRV signal so that signals related as well as unrelated to respiration can be analyzed. It is concluded that the development of new approaches to handling time-varying scenarios are warranted, as is benchmarking of performance evaluated in technical as well as in physiological/clinical terms.

The Entrainment Frequency of Cardiolocomotor Synchronization in Long-Distance Race Emerges Spontaneously at the Step Frequency

In forced conditions, where the heart rate and step frequency have been matched, cardiolocomotor synchronization (CLS) has been recognized. However, knowledge about the occurrence of CLS and its triggers in sports gesture in real contexts is little known. To address this gap, the current study tested the hypothesis that CLS in running spontaneous conditions would emerge at entrainment bands of muscle activation frequencies associated with a freely chosen step frequency. Sixteen male long-distance runners undertook treadmill assessments running ten three-minute bouts at different speeds (7, 7.5, 8, 9, 10, 11, 12, 13, 14, and 15 km⋅h^–1). Electrocardiography and surface electromyography were recorded simultaneously. The center frequency was the mean of the frequency spectrum obtained by wavelet decomposition, while CLS magnitude was determined by the wavelet coherence coefficient (WCC) between the electrocardiography and center frequency signals. The strength of CLS affected the entrainment frequencies between cardiac and muscle systems, and for WCC values greater than 0.8, the point from which we consider the emerging CLS, the entrainment frequency was between 2.7 and 2.8 Hz. The CLS emerged at faster speeds (13–15 km⋅h^–1) most prevalently but did not affect the muscle activation bands. Spontaneous CLS occurred at faster speeds predominantly, and the entrainment frequencies matched the locomotor task, with the entrainment bands of frequencies emerging around the step frequencies (2.7–2.8 Hz). These findings are compatible with the concept that interventions that determine optima conditions of CLS may potentiate the benefits of the cardiac and muscle systems synchronized in distance runners.

Effects of a 75-km mountain ultra-marathon on heart rate variability in amateur runners

BACKGROUND

This study examined the effects of a mountain ultra-marathon (MUM) on the activity of the autonomous nervous system through heart rate variability (HRV) monitoring and determined whether this variable related to final performance.

METHODS

Heart rate and HRV were measured in eight male amateur runners (aged 37-60 years). Measurements were recorded before and after the event, in resting conditions, as well as continuously throughout the whole MUM. In addition, percentage (%) of heart rate reserve (HR<inf>res</inf>) and partial and total times during the race were analyzed.

RESULTS

Average heart rate (HR<inf>avg</inf>) measured at rest was increased after the event (+37%). Standard deviation of successive differences (SDSD) and the square root of the mean squared differences of successive NN intervals (RMSSD) were reduced after the MUM (-56% and -59%, respectively). There was a positive relationship between the frequency-domain index normalized low frequency power (PLFn) measured at rest before the event and race time (0.79) while there was a negative relationship between race time and the difference in HR<inf>avg</inf> before and after the event. In the last half of the event, there was a high correlation (Spearman coefficient of correlation >0.9) between race time and the standard deviation of the NN intervals (SDNN) registered during the race.

CONCLUSIONS

Autonomous cardiac regulation can be related to the performance in a mountain ultra-marathon. HRV monitoring could represent a practical tool for the evaluation of the relationship between the autonomous nervous system activity and performance in a mountain ultra-marathon.

Electrocardiogram-Derived Tidal Volume During Treadmill Stress Test

OBJECTIVE

Electrocardiogram (ECG) has been regarded as a source of respiratory information with the main focus in the estimation of the respiratory rate. Although little research concerning the estimation of tidal volume (TV) has been conducted, there are several ECG-derived features that have been related with TV in the literature, such as ECG-derived respiration, heart rate variability, and respiratory rate. In this paper, we exploited these features for estimating TV using a linear model.

METHODS

25 young (33.4 ± 5.2 years) healthy male volunteers were recruited for performing a maximal (MaxT) and a submaximal (SubT) treadmill stress test, which were conducted on different days. Both tests were automatically segmented in stages attending to the heart rate. Afterwards, a subject-specific TV model was calibrated for each stage, employing features from MaxT, and the model was later used for estimating the TV in SubT.

RESULTS

During exercise, the different proposed approaches led to relative fitting errors lower than 14% in most of the cases and 6% in some of them.

CONCLUSION

Low achieved fitting errors suggest that TV can be estimated from ECG during a treadmill stress test.

SIGNIFICANCE

The results suggest that it is possible to estimate TV during exercise using only ECG-derived features.

Assessment of HRV after maximal exercise in trained postmenopausal women

Increased parasympathetic tone achieved with endurance training may provide cardioprotection after menopause. To compare heart rate variability (HRV) from rest through maximal exercise and recovery in trained postmenopausal women. Thirty-six postmenopausal women who self-reported training at either moderate (MOD; 3-5.9 METS; 58.9+/-4.4 year) or vigorous (VIG; >6 METS; 59.7+/-5.2 year) intensities participated. HRV was measured for 5 min in the supine position, in the last minute of the VO2max test and after 2 min of active recovery. HRV in MOD and VIG was compared using a factorial ANOVA with repeated measures on time. MOD and VIG responded similarly over the three time periods for root mean square of sequential deviations (rMSSD), and high (HF) and low frequency (LF) power (p>0.05). Maximal exercise lowered rMSSD (3.3+/-0.08 vs. 1.2+/-0.06) and lnLF (4.1+/-0.05 vs. 3.3+/-0.13) and increased lnHF (3.3+/-0.14 vs. 4.0+/-0.10; p<0.01) from resting. However, active recovery restored lnHF (3.3+/-0.11) and lnLF (4.1+/-0.08) from maximal values (p<0.01). Our findings suggest that moderate and vigorous exercise training may enhance HRV recovery following one bout of maximal exercise in older women.

Characterization and reduction of exercise-based motion influence on heart rate variability using accelerator signals and channel decoding in the time-frequency domain

OBJECTIVE

Heart rate variability (HRV) is defined as the variation of the heart's beat to beat time intervals. Although HRV has been studied for decades, its response to stress tests and off-rest measurements is still under investigation. In this paper, we studied the influence of motion on HRV throughout different exercise tests, including a maximal running of healthy recreational runners, cycling, and walking tests of healthy subjects.

APPROACH

In our proposed method, we utilized the motion trajectory (which is known to exist partially in HRV) measured by a three-channel accelerator (ACC). We then estimated their shares in HRV using a wearable electrocardiogram (ECG) and an error-correcting problem formulation. In this method, we characterized the motion components of three orthogonal directions induced into the HRV signal, and then we suppressed the estimated motion artefact to construct a motion-attenuated spectrogram. Main results and Significance: Our analysis showed that HRV in the exercise context is susceptible to motion artefacts. Furthermore, the interpretation of autonomic nervous system (ANS) activity and HRV indices throughout exercise has a high margin of error depending on the intensity level, type of exercise, and motion trajectory. Our experiment on 84 healthy subjects throughout mid-intensity cycling and walking tests showed 39% and 32% influence on average, respectively. In addition, our proposed method revealed through a maximal running test with 11 runners that motion can describe on average 20%-40% of the HRV high-frequency (HF) energy at different workloads of running.

Spectral fusion-based breathing frequency estimation; experiment on activities of daily living

BACKGROUND

We study the estimation of breathing frequency (BF) derived from wearable single-channel ECG signal in the context of mobile daily life activities. Although respiration effects on heart rate variability and ECG morphology have been well established, studies on ECG-derived respiration in daily living settings are scarce; possibly due to considerable amount of disturbances in such data. Yet, unobtrusive BF estimation during everyday activities can provide vital information for both disease management and athletic performance optimization.

METHOD AND DATA

For robust ECG-derived BF estimation, we combine the respiratory information derived from R-R interval (RRI) variability and morphological scale variation of QRS complexes (MSV), acquired from ECG signals. Two different fusion techniques are applied on MSV and RRI signals: cross-power spectral density (CPSD) estimation and power spectrum multiplication (PSM). The algorithms were tested on large sets of data collected from 67 participants during office, household and sport activities, simulating daily living activities. We use spirometer reference BF to evaluate and compare our estimations made by different models.

RESULTS AND CONCLUSION

PSM acquires the least average error of BF estimation, [Formula: see text] and [Formula: see text], compared to the reference spirometer values. PSM offers approximately 25 and 75% less error in comparison with the CPSD fusion estimation and the estimation by those two exclusive sources, respectively. Our results demonstrate the superiority of both of the fusion approaches, compared to the estimation derived from either of RRI or MSV signals exclusively.

Time-varying analysis of electrodermal activity during exercise

The electrodermal activity (EDA) is a useful tool for assessing skin sympathetic nervous activity. Using spectral analysis of EDA data at rest, we have previously found that the spectral band which is the most sensitive to central sympathetic control is largely confined to 0.045 to 0.25 Hz. However, the frequency band associated with sympathetic control in EDA has not been studied for exercise conditions. Establishing the band limits more precisely is important to ensure the accuracy and sensitivity of the technique. As exercise intensity increases, it is intuitive that the frequencies associated with the autonomic dynamics should also increase accordingly. Hence, the aim of this study was to examine the appropriate frequency band associated with the sympathetic nervous system in the EDA signal during exercise. Eighteen healthy subjects underwent a sub-maximal exercise test, including a resting period, walking, and running, until achieving 85% of maximum heart rate. Both EDA and ECG data were measured simultaneously for all subjects. The ECG was used to monitor subjects' instantaneous heart rate, which was used to set the experiment's end point. We found that the upper bound of the frequency band (Fmax) containing the EDA spectral power significantly shifted to higher frequencies when subjects underwent prolonged low-intensity (Fmax ~ 0.28) and vigorous-intensity exercise (Fmax ~ 0.37 Hz) when compared to the resting condition. In summary, we have found shifting of the sympathetic dynamics to higher frequencies in the EDA signal when subjects undergo physical activity.

Contribution of body movements on the heart rate variability during high intensity running

We studied the association between the heart rate variability (HRV) and the subject's movement during high intensity running. HRV is affected by movement, and this phenomena is known as cardiolocomotor coupling (CLC). Characterization of movement related components on the HRV spectrogram is a principal step toward meaningful interpretation of autonomic nervous system (ANS) activity. According to the literature, the aliases of the first and second harmonics of the cadence frequency are the main contributors affecting HRV. Instead, we found out that there is another aliasing component containing significant power in the HRV spectrogram. The source of this component might be the arm swings, torso movement or any other mechanical movement along the horizontal axis, orthogonal to the cadence direction. Our results show that in 13 out of 22 subjects the spectral HRV component arising from the alias of the second harmonic of cadence frequency (vertical acceleration) accommodates significantly less energy than the component related to the alias of the first harmonic of horizontal acceleration. Therefore, neglecting this component and/or considering the second harmonic of the cadence frequency as more dominant one is not always a valid assumption.

Accuracy of the Garmin 920 XT HRM to perform HRV analysis

Heart rate variability (HRV) analysis is widely used to investigate autonomous cardiac drive. This method requires periodogram measurement, which can be obtained by an electrocardiogram (ECG) or from a heart rate monitor (HRM), e.g. the Garmin 920 XT device. The purpose of this investigation was to assess the accuracy of RR time series measurements from a Garmin 920 XT HRM as compared to a standard ECG, and to verify whether the measurements thus obtained are suitable for HRV analysis. RR time series were collected simultaneously with an ECG (Powerlab system, AD Instruments, Castell Hill, Australia) and a Garmin XT 920 in 11 healthy subjects during three conditions, namely in the supine position, the standing position and during moderate exercise. In a first step, we compared RR time series obtained with both tools using the Bland and Altman method to obtain the limits of agreement in all three conditions. In a second step, we compared the results of HRV analysis between the ECG RR time series and Garmin 920 XT series. Results show that the accuracy of this system is in accordance with the literature in terms of the limits of agreement. In the supine position, bias was 0.01, - 2.24, + 2.26 ms; in the standing position, - 0.01, - 3.12, + 3.11 ms respectively, and during exercise, - 0.01, - 4.43 and + 4.40 ms. Regarding HRV analysis, we did not find any difference for HRV analysis in the supine position, but the standing and exercise conditions both showed small modifications.

Inclusion of Respiratory Frequency Information in Heart Rate Variability Analysis for Stress Assessment

Respiratory rate and heart rate variability (HRV) are studied as stress markers in a database of young healthy volunteers subjected to acute emotional stress, induced by a modification of the Trier Social Stress Test. First, instantaneous frequency domain HRV parameters are computed using time-frequency analysis in the classical bands. Then, the respiratory rate is estimated and this information is included in HRV analysis in two ways: 1) redefining the high-frequency (HF) band to be centered at respiratory frequency; 2) excluding from the analysis those instants where respiratory frequency falls within the low-frequency (LF) band. Classical frequency domain HRV indices scarcely show statistical differences during stress. However, when including respiratory frequency information in HRV analysis, the normalized LF power as well as the LF/HF ratio significantly increase during stress ( p-value 0.05 according to the Wilcoxon test), revealing higher sympathetic dominance. The LF power increases during stress, only being significantly different in a stress anticipation stage, while the HF power decreases during stress, only being significantly different during the stress task demanding attention. Our results support that joint analysis of respiration and HRV obtains a more reliable characterization of autonomic nervous response to stress. In addition, the respiratory rate is observed to be higher and less stable during stress than during relax ( p-value 0.05 according to the Wilcoxon test) being the most discriminative index for stress stratification (AUC = 88.2 % ).

Attenuation of the influence of cardiolocomotor coupling in heart rate variability interpretation during exercise test

During exercise test, cardiolocomotor coupling related components appear in heart rate variability (HRV), blurring its interpretation as autonomic nervous system (ANS) marker. These cardiolocomotor coupling related components are centered at the pedalling and running stride frequency, as well as at their aliases, and may overlap with the low frequency (LF) and high frequency (HF) components of HRV. In this work cardiolocomotor-related HRV components are studied during maximal exercise test on treadmill and cycle ergometer. Power in the bands related to cardiolocomotor coupling increases with exercise intensity in cycle ergometer but not in treadmill exercise test, where it displays higher values for all exercise intensities. A method is proposed to reduce the effect of this coupling in the interpretation of HRV. Evolution of the power in the low frequency (LF) and high frequency (HF) bands are studied after the proposed reduction of cardiolocomotor coupling, showing more significant changes with exercise intensity than before the method is applied.