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![Heart rate (HR) variability (HRV) during exercise. Representation of the R-R interval series before [RR(k) in METHODS; A] and after [m(k) in METHODS; B] removal of the decreasing trend observed in A. Note that the R-R interval variability increased from 60 to 100% of the maximal power output (Pmax), attesting to an increase in one or several HRV component(s) with workload.](profile/Gregory-Blain-2/publication/24146631/figure/fig1/AS:362646596538369@1463473095094/Heart-rate-HR-variability-HRV-during-exercise-Representation-of-the-R-R-interval.png)
Heart rate (HR) variability (HRV) during exercise. Representation of the R-R interval series before [RR(k) in METHODS; A] and after [m(k) in METHODS; B] removal of the decreasing trend observed in A. Note that the R-R interval variability increased from 60 to 100% of the maximal power output (Pmax), attesting to an increase in one or several HRV component(s) with workload.
Source publication
To test the hypothesis that cycling exercise modulates heart rate variability (HRV), we applied a short-time Fourier transform on the electrocardiogram of subjects performing a maximal graded cycling test. A pedaling frequency component (PFC) in HRV was continuously observed over the time course of the exercise test and extracted from R-R interval...
Contexts in source publication
Context 1
... due to workload increase using a polynomial fitting po(k) (order equal to 20). The second processing step was a 100th-order high-pass filter (the cutoff frequency was 0.03, with 0.5 corresponding to one-half of the normalized sampling rate) applied to RR(k), where the trend po(k) has been removed. The resulting signal was termed m(k) (example in Fig. ...
Context 2
... group differences were found in HR, F R , and VT. with our previous studies (4, 5), R-R interval significantly and continuously shortened with workload increase, whereas its variability demonstrated a biphasic evolution: it de- creased from the beginning of exercise to 60% P max and then increased during the highest intensities (example in Fig. ...
Context 3
... modulates HRV. Most protocols aiming to study HRV under exercise conditions have been limited to constant load or very slow trend ramp load to reduce the nonstationarity of R-R interval series (2, 6, 9, 24, 31, 32). From low to moderate workloads (60% P max ), these studies consistently reported a marked decrease in the overall HRV spectral power following vagal withdrawal (2,4,9,31,38). ...
Context 4
... although HRV is mainly mediated by input from the autonomic nervous system at rest (1,2,34,36), a neural contribution to A PFC increase with increases in workload is unlikely because, not only is vagal withdrawal nearly complete at mild workloads (11,13,37), but both the sympathetic and parasympathetic nervous systems respond too slowly to mod- ulate HRV in the pedaling frequency range (1, 3, 35). ...
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Little research addresses how parental self-efficacy is related to stress responses, and no research does so among parents of early adolescents. To fill this research gap, the current study examined the association between maternal self-efficacy and physiological stress responses during early adolescence. Participants were 68 mother–early adolescen...
Although the generation mechanism of the low-frequency (LF) component of heart rate variability (HRV) is controversial, HRV is a potential candidate in designing objective measurement methodologies for emotions. These methodologies could be valuable for several biosignal applications. Here, we have conducted a simulation analysis using a novel math...
[Purpose] The purpose of this study was to examine changes in vagal tone during passive exercise while supine. [Subjects and Methods] Eleven healthy males lay supine for 5 min and then performed passive cycling for 10 min using a passive cycling machine. The lower legs moved through a range of motion defined by 90° and 180° knee joint angles at 60...
Background: Stress is a motivator to consume alcohol, a well-documented relapse risk, and is known to differentially affect biological and psychological processes as people age.
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Purpose
The deep breathing test (DBT) is a sensitive test of cardiovagal function. The aim of this study was to explore associations between physical activity and sedentary time, measured by accelerometer, and autonomic function, using DBT.
Methods
In the Swedish Cardio-Pulmonary bioImage Study, men and women aged 50–64 were randomly invited from...
Citations
... The most important property of Schumann resonance is that the electromagnetic field on earth and the physiological rhythm of the body can be synchronized (Alabdulgader, 2021;Alabdulgader et al., 2018;McCraty et al., 2017;Miller & Lonetree, 2013;Mitsutake et al., 2005). According to another paper on synchronization, physiological rhythms are changed similar to the frequency of certain repetitive movements (Blain et al., 2009;Daffertshofer et al., 2004), and visual and sound stimulation (Anishchenko et al., 2000;Bernardi et al., 2009). Based on this, this study suggests a hypothesis. ...
Objectives
In this study, we explain the role of enhancing the stability and homeostasis of the autonomic nervous system (ANS) by proposing the average heart rate sound resonance (aHRSR), a sound stimulation to prevent imbalance of ANS due to dynamic movement. The effect of aHRSR on ANS was analyzed through the time and frequency domain of heart rate variability (HRV) using the photoplethysmogram data (PPG) of 22 participants (DUIRB‐202109‐12).
Method
When the subjects performed dynamic movements that could cause changes in the ANS, HRV indicators using PPG data for 5 min before and after the movements were analyzed according to the presence or absence of aHRSR. The standard deviation of the NN intervals (SDNN), the square root of the mean squared differences of the NN intervals (RMSSD), low‐frequency band (LF), and high‐frequency band (HF), which represent sympathetic and parasympathetic nerve activity, were used as indicators, where SNDD and LF represent total ANS and sympathetic activity, while RMSSD and HF represent parasympathetic activity.
Results
As the effects of aHRSR on dynamic movement, the recovery time of RR interval was advanced by about 15 s, SDNN increased from ([44.16 ± 13.11] to [47.85 ± 15.16]) ms, and RMSSD increased from ([23.73 ± 9.95] to [31.89 ± 12.48]) ms ( p < 0.05), increasing the stability of the ANS and reducing instability. The effect of homeostasis of the ANS according to aHRSR is also shown in reducing the change rate of LF from (−13.83 to −8.83) %, and the rate of change of HF from (10.59 to 3.27) %.
Conclusions
These results suggest that aHRSR can affect the cardiovascular system by assisting physiological movements that occur during dynamic movement.
... The most important thing about Schumann resonance is that the electromagnetic field on earth and the physiological rhythm of the body can be synchronized [26][27][28][29][30]. According to another paper on synchronization, physiological rhythms are changed similar to the frequency of certain repetitive movements [31,32], visual and sound stimulation [33,34]. Based on this, this study suggests a hypothesis. ...
... In the recovery phase, SDNN and RMSSD maintain higher values during sound stimulation for sections 20 to 22 (Fig 4. b-c). Additionally, Shown in Figure 4.c, the base line of RMSSD significantly increased during sound stimulation of aHRSR (sections [24][25][26][27][28][29][30][31][32][33][34][35][36][37]. These changes indicate that aHRSR stimulation not only affects the instantaneous changes in the ANS, but also continuously affects the ANS. ...
Objectives: In this study, we explain the role of respiratory support by proposing aHRSR, a sound stimulation to prevent imbalance of ANS due to dynamic movement. The function and role of aHRSR was analyzed through the time and frequency domain of HRV using PPG data of 22 participants (DUIRB-202109-12). Method: Changes in the ANS were confirmed using SDNN and LF, which represent total ANS activity, and the correlation between the stimulation effect of aHRSR and respiration was analyzed using RMSSD and HF, which included parasympathetic nervous system and respiratory frequency. Results : As an effect of aHRSR on dynamic movement, the recovery time of RR interval was advanced by about 15 seconds, SDNN increased from 44.16 to 47.85 ms, and RMSSD increased from 23.73 to 31.89 ms, increasing the stability of the ANS and reducing instability. Also, by reducing the change rate of LF from -13.83 to -8.83 % and the rate of change of HF from 10.59% to 3.27%, the effect of homeostasis of the ANS according to aHRSR is also shown. In other words, aHRSR decreased HF including respiratory energy and increased RMSSD generated during stimulation of the parasympathetic nervous system. Conclusions : These results suggest that aHRSR can stimulate the parasympathetic nervous system with minimal changes in breathing by assisting breathing that occurs during dynamic movement.
... The most commonly used mathematical technique for frequency domain analysis is the fast Fourier transform, which was used with our data. While reliable at rest, these measures are broadly considered problematic during exercise due to the dramatic reduction in these variables, increased respiratory frequency, the potential effect of cardiolocomotor coupling (entrainment) with the step cycle or pedaling frequency component [3,4], and the lack of an agreed upon suitable alternative approach to processing the data [5]. Alternatives such as broadening the HF band or centering HF to a fixed respiratory frequency have been considered, as have more advanced techniques involving signal decomposition [6]. ...
Purpose
Assessment of heart rate variability (HRV) during exercise is an emerging area of study. Measurements of HRV are characterized by domain (time, frequency, and nonlinear) or time scale (short or long-term). The purpose of this study was to compare methods of HRV analysis during submaximal running before and after 18 weeks of marathon training.
Methods
Pre and post-training, 42 recreational runners (21.1 ± 1.5 years, 28 f) completed a 2-mile (3218 m) time trial (2MI) and a submaximal run (SUBMAX) at 75% of 2MI speed. Changes in HRV during SUBMAX and physiological measures were assessed with paired samples t tests. Correlations between the changes of selected variables were assessed with Spearman’s rho.
Results
Long-term measures of HRV did not change with training, while several short-term measures changed: Poincaré plot SD1 (PPSD1) (2.9 ± 0.9, 3.5 ± 1.5; p = 0.049), PPSD1.PPSD2⁻¹ (0.23 ± 0.11, 0.29 ± 0.17; p = 0.047) and Detrended Fluctuation Analysis α1 (0.95 ± 0.25, 0.82 ± 0.28; p = 0.006). Both 2MI (15.6 ± 2.0 min, 14.2 ± 1.7 min; p ≤ 0.001) and VO2max (50.6 ± 7.6, 52.7 ± 7.3; p = 0.049) improved with training. Change in sample entropy (SampEn) was correlated with change in 2MI (rho = − 0.379, p = 0.016), suggesting an association between this measure of HRV and endurance performance.
Conclusions
Measures of short-term HRV change with marathon training and may reflect adaptation independent of change in aerobic capacity.
... During exercise stress testing, performed either by pedaling a bicycle ergometer or running on a treadmill, the interpretation of the HRV spectrum is complicated by the appearance of a spurious locomotor-related component centered at the pedaling or running stride frequency F l (t). This component is observed during a maximal graded bicycle ergometer stress test, particularly at higher workloads when the locomotor-heart rate coupling (synchronization) is accentuated [109]. The coupling may be explained as a consequence of heart rate entrainment by locomotor rhythms due to interaction [110]. ...
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.
... Slower respiration with larger tidal volumes increases RSA, and the use of prescribed or "paced" breathing yields different values than measurements taken without paced breathing [8] and is considered a common source of discrepant values in the assessment of HRV [9]. During exercise, coupling (or entrainment) of repetitive movement patterns, such as step rate or cycling cadence, with respiratory rate may occur, complicating the interpretation of HRV due to the effect of respiratory modulating effects [10]. ...
Heart rate variability reflects fluctuations in the changes in consecutive heartbeats, providing insight into cardiac autonomic function and overall physiological state. Endurance athletes typically demonstrate better cardiac autonomic function than non-athletes, with lower resting heart rates and greater variability. The availability and use of heart rate variability metrics has increased in the broader population and may be particularly useful to endurance athletes. The purpose of this review is to characterize current practices and applications of heart rate variability analysis in endurance athletes. Important considerations for heart rate variability analysis will be discussed, including analysis techniques, monitoring tools, the importance of stationarity of data, body position, timing and duration of the recording window, average heart rate, and sex and age differences. Key factors affecting resting heart rate variability will be discussed, including exercise intensity, duration, modality, overall training load, and lifestyle factors. Training applications will be explored, including heart rate variability-guided training and the identification and monitoring of maladaptive states such as overtraining. Lastly, we will examine some alternative uses of heart rate variability, including during exercise, post-exercise, and for physiological forecasting and predicting performance.
... La cadencia de pedaleo también puede influir en los parámetros de la VFC, Blain et al., (62) realizaron un estudio en tres diferentes cadencias de pedaleo 70, 80 y 90 rpm, participaron 15 hombres no entrenados en ciclismo, todos con edades entre 27.3 ± 7 años, encontraron El primer minuto de recuperación se considera como reactivación parasimpática (59) aunque alguna evidencia ha sugerido participación simpática también (60). En este sentido, Nakamura & Aguiar (61) realizaron un estudio sobre el índice de reactivación cardiovascular, evaluando los primeros 30 seg posterior al ejercicio (4min de ejercicio al 80% del umbral ventilatorio), durante fase folicular temprana y la fase media lútea del ciclo menstrual en ocho mujeres entrenadas en resistencia y 10 no entrenadas, todas con edades entre 21.6±0.2 ...
... En el presente estudio la cadencia para hombres fue de 90rpm y para mujeres fue ˃65rpm. Sin embargo, los participantes del estudio de Blain (62) no eran ciclistas entrenados, pues los ciclistas profesionales hombres se sienten muy cómodos con cadencias de pedaleo de 90-110 rpm y las mujeres con cadencias de 70-80 rpm. Finalmente, el tipo de calentamiento previo al test parece no tener ningún efecto sobre el rendimiento, en los índices de la VFC de acuerdo con los resultados encontrados por Dos-Santos et al., (32), en el que realizaron un test máximo incremental, previo a este realizaron tres tipos de calentamiento, uno sin calentamiento, dos con calentamiento y tres estiramiento balístico, en el que participaron 9 hombres no entrenados, con edades entre 22±1 años. ...
Introducción: Este estudio muestra las diferencias en la variabilidad de la frecuencia cardiaca (VFC) entre hombres y mujeres ciclistas profesionales, durante un test máximo incremental. Objetivo: Evaluar la VFC en ciclistas profesionales hombres y mujeres de pista durante un test máximo incremental de 12min. Métodos: Participaron 18 ciclistas profesionales que compiten en pruebas de pista, 7 mujeres edad: 22±5.9 años; años de entrenamiento 5.8±2.9 y 11 hombres edad: 21.4±4.4 años; años de entrenamiento 6.7±3.3. El test incremental para hombres inició con 100 vatios e incrementos de 50 vatios hasta 300 vatios, luego incrementos de 20 vatios, cada 2 minutos, cadencia pedaleo 75-90 rpm. Las mujeres iniciaron con 50 vatios, incrementos de 50 vatios hasta 200 vatios, luego incrementos de 20 vatios, cada 2 minutos, cadencia pedaleo 65-80 rpm. Resultados: Primera etapa, no existió diferencia significativa de la VFC entre los grupos. Segunda etapa, RMSSD fue mayor significativamente en las mujeres. Tercera etapa, LF/HF fue significativamente mayor en hombres. Cuarta a sexta etapa, no existió diferencia significativa en la VFC. Durante el primer minuto de recuperación, no existió diferencia significativa de la VFC. Conclusiones: Durante un test incremental en ciclistas entrenados en velocidad se observan diferencias significativas en la VFC pero a diferentes intensidades de ejercicio entre hombres y mujeres. En cambio, a la misma intensidad de ejercicio no existen diferencias significativas en la VFC, al igual que en el primer minuto de recuperación. Esto indica que en la evaluación de la VFC donde se incluyan hombres y mujeres el protocolo del test incremental debe ser exactamente el mismo.
... La VFC a également été étudiée durant les exercices physiques (Yamamoto et al. 1991;Tulppo et al. 1996;Hautala et al. 2003;Cottin et al. 2004Cottin et al. , 2007Blain et al. 2005Blain et al. , 2009Leicht et al. 2008;Di Michele et al. 2012;Michael et al. 2017a (Yamamoto et al. 1991;Tulppo et al. 1996;Hautala et al. 2003;Leicht et al. 2008;Martinmäki et al. 2008). Enfin, il semble possible de détecter, grâce au phénomène d'arythmie sinusale respiratoire, les SV1 et SV2, au cours d'exercices à intensité croissante (Blain et al. 2005;Cottin et al. 2006;Buchheit et al. 2007c;Cassirame et al. 2015). ...
... Pour s'affranchir de cette contrainte, il est possible de réaliser la mesure lors d'exercices à intensité constante et avec une fréquence gestuelle stable lorsque cela est réalisable(Cottin et al. 2004). En cas de non stabilité de la FC, il est tout de même possible de réaliser l'analyse sur un segment stable en supprimant la courbe de tendance de fond d'augmentation de la FC(Blain et al. 2009). Par ailleurs, les modalités d'exercice ainsi que les durées que les participants ont passé à chaque intensité d'exercice varient d'une étude à l'autre. ...
Firefighting interventions lead to high psychophysiological stress in firefighters, which causes a significant increase in the risk of death from pathological causes. The current challenge is to preserve the health and safety of firefighters through better knowledge of their physiological and psychological responses at work. Thus, the objective of this doctoral work was to investigate the psychophysiological responses of firefighters during simulated firefighting interventions, in ecological conditions.
The main results of study 1 showed that in addition to the high physiological stress and the disruption of executive functions during the firefighting intervention, the post-intervention phase is marked by very low cardiac parasympathetic activity. However, although the breathing apparatus increased the duration of the intervention, as well as the cardiac stress and the perceived exertion, it did not influence the cardiac parasympathetic reactivation.
The next two studies were methodological. Study 2 has quantified the workload generated by a firefighting intervention. The results revealed that subjective quantification of the workload is the most sensitive method to discriminate different equipment conditions and therefore seems more suitable for monitoring the workload of firefighters. Then, study 3 tested the reliability and the sensitivity of post-exercise analysis of ultra-short-term heart rate variability in order to facilitate its use in ecological conditions. However, the results showed a low sensitivity of ultra-short-term heart rate variability analysis, and therefore suggest to keep a conventional short-term analysis.
After showing a psychophysiological stress and a significant parasympathetic disturbance during a simulated daytime intervention, we showed in study 4 that psychophysiological stress was greater at night than during the day, both during the alarm phase and the recovery phase. Finally, in study 5, the investigation of a more delayed phase of recovery, during the sleep of firefighters, showed that nocturnal cardiac autonomic activity was disturbed during nights when firefighters are on-call, with and without interventions.
This doctoral work provides new knowledge on the psychophysiological responses of firefighters during firefighting interventions, and in particular on nocturnal and post-intervention cardiac autonomic regulation. The results of this thesis provide recommendations on the use of firefighter monitoring tools and open up new research perspectives aimed at reducing their psychophysiological stress.
Key words: Firefighters, Firefighting, Recovery, Exercise physiology, Autonomic control of the heart, Heart rate variability, Executive functions
... The concept behind the time-frequency joint representation is to distribute the signals into small parts followed by analysis of separate parts. In this way, the analysed signal gives more information about different frequencies [124][125][126][127][128][129]. ...
In time–frequency analysis, generalization of S-transform (ST) is known as fractional S-transform. Recently, fractional S-transform (FrST) has played an important role in the area of signal and image processing. The ST is a hybrid of wavelet, and short time Fourier transform. In this paper, the definition, properties and applications areas of ST and FrST are focused. The aim of this survey is to study ST and FrST, formats, properties, applications, and open issues to encourage further research in the fields of digital signal processing (DSP) and other applications area of engineering. In this article, firstly the several transforms that are related to ST as well as FrST are described and in the second part, comprehensive and exhaustive facts on the use of S-transform reviews and FrST in the area of DSP are enlightened. This transform technique is used for detection of LFM signal in the presence of echo.
... Moreover, the actual proportion of motion artefacts has still not been very well investigated. One major source of HRV contamination is known to be cadence (Blain et al 2009, Bailón et al 2013. Recently, the study of HRV during stress tests has been of interest (Lunt et al 2011, Magagnin et al 2011, Bailón et al 2013, Jarchi and Casson 2016, Alikhani et al 2017, Hernando et al 2017. ...
... Recently, the study of HRV during stress tests has been of interest (Lunt et al 2011, Magagnin et al 2011, Bailón et al 2013, Jarchi and Casson 2016, Alikhani et al 2017, Hernando et al 2017. In 2009, Blain et al (2009 revealed the existence of cardio-locomotion coupling (CLC) components in HRV during cycling. They observed an increasing HF energy band associated with pedalling frequency in the HRV spectral content. ...
... Recently, the study of HRV during stress tests has been of interest (Lunt et al 2011, Magagnin et al 2011, Bailón et al 2013, Jarchi and Casson 2016, Alikhani et al 2017, Hernando et al 2017. In 2009, Blain et al (2009 revealed the existence of cardio-locomotion coupling (CLC) components in HRV during cycling. They observed an increasing HF energy band associated with pedalling frequency in the HRV spectral content. ...
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.
... However, most of the research in this discipline has contributed to the resting state EDR [10][11][12]. What is more, severe challenges are introduced to ECG processing in physical activity contexts, including variable mean heart rate (HR), high level of movement artifacts and introduction of cardio-locomotion coupling (CLC) components [13,14]. ...
... In the spectral analysis this could lead into misguided detection of BF at this lower frequency, instead of the higher actual one. Speaking of which, a prevalent factor that might influence the spectral interpretation of HRV signal is CLC components that arise from cadence during walking or running; or pedaling frequency during cycling [13,14]. Because of the mentioned aliasing phenomenon, these components will fold back to the HF range when they exceed the Nyquist frequency. ...
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.
