Ultra-shortened time-domain HRV parameters at rest and following exercise in athletes: an alternative to frequency computation of sympathovagal balance

Esco, Michael R.; Williford, Henry N.; Flatt, Andrew A.; Freeborn, Todd J.; Nakamura, Fábio Yuzo

doi:10.1007/s00421-017-3759-x

Cited by 50 publications

(46 citation statements)

References 29 publications

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“…Although the values of the indices of parasympathetic reactivation vary from one study to another, the present values are similar to those observed in previous work after high‐intensity exercises. For instance, after a maximal incremental test and high‐intensity exercises, T30 ranged between 261 and 295 seconds, HRR 60 seconds ranged between 36 and 39 bpm, and LnRMSSD 5‐10 was 1.60 ms . In comparison with previous studies, our post‐exercise parasympathetic reactivation values were similar to those for high‐intensity exercises and lower‐ to moderate‐intensity exercises (eg, HRR 60 seconds : 54 bpm; T30: 144 seconds).…”

Section: Discussionsupporting

confidence: 81%

“…1.60 ms. 30 In comparison with previous studies, our postexercise parasympathetic reactivation values were similar to those for high-intensity exercises and lower-to moderate-intensity exercises (eg, HRR 60 seconds : 54 bpm; T30: 144 seconds 14 ). While no correlations were found between the parasympathetic reactivation indices and mean HR, LnRMSSD 5-10 minutes was positively correlated with the time spent in the low-intensity zone (HR Z1) and negatively correlated with the time spent in the high-intensity zone (HR Z3).…”

supporting

confidence: 87%

“…When non-normal distribution was observed (RMSSD), a log transformation (ln) was applied (LnRMSSD). 30 Differences in completion time, RPE, HR, BF, MCOM, reaction time, and wrong answers between the three rescue interventions were analyzed using one-way repeated-measures analysis of variance (ANOVA), with the factor condition (PPC, SCBA, SCBAc). Differences in T30, HRR 60-300 seconds , SDNN 5-10 minutes , and LnRMSSD 5-10 minutes between the four experimental sessions were analyzed using one-way repeated-measures ANOVA with the factor condition (PPC, SCBA, SCBAc, IFT).…”

Section: Discussionmentioning

confidence: 99%

“…Normality distribution of the data was verified using the Shapiro‐Wilk test. When non‐normal distribution was observed (RMSSD), a log transformation (ln) was applied (LnRMSSD) . Differences in completion time, RPE, HR, BF, MCOM, reaction time, and wrong answers between the three rescue interventions were analyzed using one‐way repeated‐measures analysis of variance (ANOVA), with the factor condition (PPC, SCBA, SCBAc).…”

Section: Methodsmentioning

confidence: 99%

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Physiological responses and parasympathetic reactivation in rescue interventions: The effect of the breathing apparatus

Marcel-Millet

Ravier

Grosprêtre

et al. 2018

Scandinavian Med Sci Sports

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This study aimed to assess the effect of wearing a breathing apparatus during a simulated rescue intervention on psychophysiological responses and parasympathetic reactivation of firefighters. Thirty-four firefighters participated in this study which consisted of four experimental sessions conducted randomly: a maximal fitness test and three rescue interventions performed (a) with personal protective clothing (PPC); (b) with PPC and the full self-contained breathing apparatus (SCBA), including cylinder, full-face piece, and breathing regulator; and (c) with PPC and only the cylinder of the self-contained breathing apparatus (SCBAc). Physiological (heart rate [HR], breathing frequency [BF]) and perceptual (rating of perceived exertion [RPE]) responses were continuously collected during the three rescue interventions. Parasympathetic reactivation was assessed using HR recovery and variability indexes after experimental sessions. HR responses ranged between 63% and 95% of HR , and BF responses ranged between 22 and 55 breaths/min for the different activity tasks. Parasympathetic reactivation indexes were similar for the rescue interventions but lower than after the intermittent fitness test (P = 0.016 - P < 0.0001). Mean HR for both SCBAc (83.2 ± 4.1%HR ) and SCBA (83.1 ± 5.2%HR ) was higher in comparison with PPC (79.5 ± 5.3%HR ). RPE was higher for SCBA than for SCBAc which was higher than PPC. Mean BF for SCBA (34 breaths/min) was lower than PPC (40 breaths/min) and SCBAc (43 breaths/min). Based on HR, BF, and RPE, rescue interventions seem to be psychologically and physiologically stressful. Parasympathetic reactivation after PCC, SCBA, and SCBAc suggests that these conditions induce higher cardiac stress than the maximal fitness test. The study showed that SCBA increased psychophysiological perturbations.

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Section: Discussionsupporting

confidence: 81%

supporting

confidence: 87%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

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Physiological responses and parasympathetic reactivation in rescue interventions: The effect of the breathing apparatus

Marcel-Millet

Ravier

Grosprêtre

et al. 2018

Scandinavian Med Sci Sports

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“…e time domain methods are used to capture the short-, medium-, and long-term variations present in the physiological signals and systems, whereas to capture the dynamics present in different spectra, frequency domain features are computed. ere are literature evidences [32,33] for patients who suffered from different variability dysfunctions [34][35][36][37][38][39][40], including heart rate variability, breathing, depression, pulse variability, insomnia problems, and epilepsy.…”

Section: Bba Linear Methodsmentioning

confidence: 99%

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

Hussain

Awan

Aziz

et al. 2020

BioMed Research International

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The adaptability of heart to external and internal stimuli is reflected by the heart rate variability (HRV). Reduced HRV can be a predictor of negative cardiovascular outcomes. Based on the nonlinear, nonstationary, and highly complex dynamics of the controlling mechanism of the cardiovascular system, linear HRV measures have limited capability to accurately analyze the underlying dynamics. In this study, we propose an automated system to analyze HRV signals by extracting multimodal features to capture temporal, spectral, and complex dynamics. Robust machine learning techniques, such as support vector machine (SVM) with its kernel (linear, Gaussian, radial base function, and polynomial), decision tree (DT), k-nearest neighbor (KNN), and ensemble classifiers, were employed to evaluate the detection performance. Performance was evaluated in terms of specificity, sensitivity, positive predictive value (PPV), negative predictive value (NPV), and area under the receiver operating characteristic curve (AUC). The highest performance was obtained using SVM linear kernel (TA = 93.1%, AUC = 0.97, 95% CI [lower bound = 0.04, upper bound = 0.89]), followed by ensemble subspace discriminant (TA = 91.4%, AUC = 0.96, 95% CI [lower bound 0.07, upper bound = 0.81]) and SVM medium Gaussian kernel (TA = 90.5%, AUC = 0.95, 95% CI [lower bound = 0.07, upper bound = 0.86]). The results reveal that the proposed approach can provide an effective and computationally efficient tool for automatic detection of congestive heart failure patients.

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Investigation on the Dependencies Between HRV, Physical Training, and Focus of Attention in Virtual Environment

Ščiglinskas

Mačiukas

Vidugirienė

et al. 2019

Communications in Computer and Information Science

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Ultra-shortened time-domain HRV parameters at rest and following exercise in athletes: an alternative to frequency computation of sympathovagal balance

Abstract: The findings of the study suggest that ultra-shortened time-domain markers may be useful surrogates of the frequency-domain parameters for tracking changes in sympathovagal activity in athletes.

Cited by 50 publications

References 29 publications

Physiological responses and parasympathetic reactivation in rescue interventions: The effect of the breathing apparatus

Physiological responses and parasympathetic reactivation in rescue interventions: The effect of the breathing apparatus

Detecting Congestive Heart Failure by Extracting Multimodal Features and Employing Machine Learning Techniques

Investigation on the Dependencies Between HRV, Physical Training, and Focus of Attention in Virtual Environment

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