Developmental differences in the effects of alcohol and stress on heart rate variability

Saalfield, Jessica; Spear, Linda P.

doi:10.1016/j.physbeh.2014.05.037

Cited by 10 publications

(4 citation statements)

References 89 publications

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“…Again, because of the two species’ histological and molecular similarities, we believed that if PF‐MF exposure affects human nerve tissue, it may also affect rat nerve tissue. In addition, like in humans, HRV analysis may be used as an indicator to evaluate the autonomic nervous system in rats [Carnevali and Sgoifo, ; Saalfield and Spear, ].…”

Section: Discussionmentioning

confidence: 99%

Investigation of effects of short‐term exposure to 50 HZ magnetic field on central, peripheral, and autonomic nervous systems in rats

Elmas

Çömlekçi

2015

Bioelectromagnetics

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Although a large number of exposure studies have been carried out to investigate possible detrimental effects of a power-line frequency magnetic field (PF-MF; 50-60 Hz) on living organisms, there is no consensus in reported results. Recent research suggested that short-term exposure to a 50 Hz PF-MF can have a greater effect than long-term exposure. In our study, we aimed to investigate the possible effect of short-term 50 Hz PF-MF exposure on central, peripheral, and autonomic nervous systems through electroencephalogram (EEG), nerve conduction velocity (NCV), and heart rate variability (HRV) measurements. To that end, Wistar albino rats and sciatic nerves gathered from the rats were exposed to a 50 Hz, 0.3 mT MF, and changes on EEG, NCV, and HRV were investigated. According to data we gathered, MF frequency and magnetic flux density that we applied did not affect EEG, NCV, or HRV. The reason for conflicting results gathered from MF exposure studies may be that the nervous system can be affected by a specific MF frequency and magnetic flux density, but that different MF exposures do not affect it. Moreover, exposure duration to MF may also have influenced results.

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Section: Discussionmentioning

confidence: 99%

Investigation of effects of short‐term exposure to 50 HZ magnetic field on central, peripheral, and autonomic nervous systems in rats

Elmas

Çömlekçi

2015

Bioelectromagnetics

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“…In control group, HRV was measured telemetrically before the experiment itself in awake and freely moving male animals (Carll et al, 2012; Choudhary et al, 2018; Couderc et al, 2002; Fazan Jr. et al, 2015; Hazari et al, 2021; Imai et al, 2008; Koizumi et al, 2011; Lamb et al, 2012; Lin et al, 2016; Mamalyga, 2013; Mangin et al, 1998; Pereira‐Junior et al, 2006, 2010; Saalfield & Spear, 2014; Shi et al, 2017; Soler et al, 2018; Towa et al, 2004; Tsai et al, 2020; Yang et al, 2019; Zajączkowski et al, 2014). If we assume that the location of implantation (subcutaneous on the back or intra‐abdominal) and the length of recovery do not play a role, then theoretically we can assume that these values could approach the “missing” reference values, although unfortunately, only for males and at the given time of measurement.…”

Section: Hrv: Control (Baseline) Values From Frequency‐domain Analysismentioning

confidence: 99%

“…Similar to investigations that analyzed changes in autonomic nervous system activity using frequency‐domain analysis, other studies have used time‐domain analysis of HRV. As in frequency‐domain studies, baseline values not only in sham groups were used (Abulaiti et al, 2011; De La Fuente et al, 2013; Houshmand et al, 2017; Krüger et al, 1997, 2000; Lima et al, 2018; Müller‐Ribeiro et al, 2017; Sallam et al, 2016, 2017; Silva, Geraldini, et al, 2017; Silva, Silva, et al, 2017; Soler et al, 2018; Wang et al, 2016; Xu et al, 2020), but also from groups in which the values were recorded only before the experimental intervention itself and the animals did not undergo surgical preparation (control) (Barbier et al, 2006; Beckers et al, 2006; Beltrán et al, 2020; Couderc et al, 2002; Farraj et al, 2009; Fazan Jr. et al, 2015; Hashimoto et al, 1999; Hazari et al, 2021; Koresh et al, 2016; Lamb et al, 2012; Lin et al, 2016; Maida et al, 2017; Mangin et al, 1998; Mostarda et al, 2009; Pereira‐Junior et al, 2010; Powell et al, 2021; Ramaekers et al, 2002; Ribeiro et al, 2021; Saalfield & Spear, 2014; Scridon et al, 2012; Shi et al, 2017; Simoes et al, 2016; Zajączkowski et al, 2018).…”

Section: Hrv: Control (Baseline) Values From Frequency‐domain Analysismentioning

confidence: 99%

Heart rate variability in male rats

Švorc,

Grešová,

Švorc

2023

Physiological Reports

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The cardiovascular system is primarily controlled by the autonomic nervous system, and any changes in sympathetic or parasympathetic activity also have an impact on myocardial activity. Heart rate variability (HRV) is a readily available metric used to assess heart rate control by the autonomic nervous system. HRV can provide information about neural (parasympathetic, sympathetic, reflex) and humoral (hormones, thermoregulation) control of myocardial activity. Because there are no relevant reference values for HRV parameters in rats in the scientific literature, all experimental results are only interpreted on the basis of changes from currently measured control or baseline HRV values, which are, however, significantly different in individual studies. Considering the significant variability of published HRV data, the present study focused primarily on comparing control or baseline HRV values under different conditions in in vivo experiments involving rats. The aim of the study was therefore to assess whether there are differences in the starting values before the experiment itself.

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“…The heartbeat signal and blood pressure are regulated by the autonomic nervous system (ANS), which is responsible for the fight-or-flight response, our body's main physiological response to stress [9]. Based on this, several studies have used heart rate variability (HRV) [5], [10]- [12] or blood pressure as potential indicators of stress. In our recent studies, we have demonstrated that the remotely measured HRV and dPTT can be used for remote stress detection.…”

Section: Introductionmentioning

confidence: 99%

Robustness of remote stress detection from visible spectrum recordings

et al. 2016

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In our recent work, we have shown that it is possible to extract high fidelity timing information of the cardiac pulse wave from visible spectrum videos, which can then be used as a basis for stress detection. In that approach, we used both heart rate variability (HRV) metrics and the differential pulse transit time (dPTT) as indicators of the presence of stress. One of the main concerns in this analysis is its robustness in the presence of noise, as the remotely acquired signal that we call blood wave (BW) signal is degraded with respect to the signal acquired using contact sensors. In this work, we discuss the robustness of our metrics in the presence of multiplicative noise. Specifically, we study the effects of subtle motion due to respiration and changes in illumination levels due to light flickering on the BW signal, the HRV-driven features, and the dPTT. Our sensitivity study involved both Monte Carlo simulations and experimental data from human facial videos, and indicates that our metrics are robust even under moderate amounts of noise. Generated results will help the remote stress detection community with developing requirements for visual spectrum based stress detection systems.

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Developmental differences in the effects of alcohol and stress on heart rate variability

Cited by 10 publications

References 89 publications

Investigation of effects of short‐term exposure to 50 HZ magnetic field on central, peripheral, and autonomic nervous systems in rats

Investigation of effects of short‐term exposure to 50 HZ magnetic field on central, peripheral, and autonomic nervous systems in rats

Heart rate variability in male rats

Robustness of remote stress detection from visible spectrum recordings

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