Nocturnal Difference in Ultra-Low Frequency Band of the Heart Rate Variability of Patients Stratified by Kampo Medicine Prescription

The aim of our study is to measure very low frequency band (VLF), low frequency band (LF) and high frequency band (HF) components of R-R interval during orthostatic experiment in panic disorder patients before and after treatment.MethodsWe assessed heart rate variability in 19 patients with panic disorder before and after 6-weeks treatment with antidepressants combined with CBT and 18 healthy controls. They were regularly assessed on the CGI, BAI and BDI. Heart rate variability was assessed during 5 min standing, 5 min supine and 5 min standing positions before and after the treatment. Power spectra were computed using a fast Fourier transformation for very low frequency - VLF (0.0033 - 0.04 Hz), low-frequency - LF (0.04-0.15 Hz) and high frequency - HF (0.15-0.40 Hz) powers.Results19 panic disorder patients entered a 6-week open-label treatment study with combination of SSRI and cognitive behavioral therapy. A combination of CBT and pharmacotherapy proved to be the effective treatment of patients. They significantly improved in all rating scales. There were highly statistical significant differences between panic patients and control group in all components of power spectral analysis in 2nd and in two component of 3rd (LF and HF in standing) positions. There was also statistically significant difference between these two groups in LF/HF ratio in supine position (2nd). During therapy there was tendency to increasing values in all three positions in components of HRV power spectra, but there was only statistically significant increasing in HF1 component.Supported by project IGA MZ ČR NS 10301-3/2009

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Beating Rate Variability of Isolated Mammal Sinoatrial Node Tissue: Insight Into Its Contribution to Heart Rate Variability

Frontiers in Neuroscience ◽

10.3389/fnins.2020.614141 ◽

2021 ◽

Vol 14 ◽

Author(s):

Ori Shemla ◽

Kenta Tsutsui ◽

Joachim A. Behar ◽

Yael Yaniv

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Frequency Band ◽

Sinoatrial Node ◽

Low Frequency ◽

Relative Increase ◽

Test Mouse ◽

Beating Rate

BackgroundBecause of the complexity of the interaction between the internal pacemaker mechanisms, cell interconnected signals, and interaction with other body systems, study of the role of individual systems must be performed under in vivo and in situ conditions. The in situ approach is valuable when exploring the mechanisms that govern the beating rate and rhythm of the sinoatrial node (SAN), the heart’s primary pacemaker. SAN beating rate changes on a beat-to-beat basis. However, to date, there are no standard methods and tools for beating rate variability (BRV) analysis from electrograms (EGMs) collected from different mammals, and there is no centralized public database with such recordings.MethodsWe used EGM recordings obtained from control SAN tissues of rabbits (n = 9) and mice (n = 30) and from mouse SAN tissues (n = 6) that were exposed to drug intervention. The data were harnessed to develop a beat detector to derive the beat-to-beat interval time series from EGM recordings. We adapted BRV measures from heart rate variability and reported their range for rabbit and mouse.ResultsThe beat detector algorithm performed with 99% accuracy, sensitivity, and positive predictive value on the test (mouse) and validation (rabbit and mouse) sets. Differences in the frequency band cutoff were found between BRV of SAN tissue vs. heart rate variability (HRV) of in vivo recordings. A significant reduction in power spectrum density existed in the high frequency band, and a relative increase was seen in the low and very low frequency bands. In isolated SAN, the larger animal had a slower beating rate but with lower BRV, which contrasted the phenomena reported for in vivo analysis. Thus, the non-linear inverse relationship between the average HR and HRV is not maintained under in situ conditions. The beat detector, BRV measures, and databases were contributed to the open-source PhysioZoo software (available at: https://physiozoo.com/).ConclusionOur approach will enable standardization and reproducibility of BRV analysis in mammals. Different trends were found between beating rate and BRV or HRV in isolated SAN tissue vs. recordings collected under in vivo conditions, respectively, implying a complex interaction between the SAN and the autonomic nervous system in determining HRV in vivo.

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MRI-related anxiety in healthy individuals, intrinsic BOLD oscillations at 0.1 Hz and heart rate variability in low frequency bands

10.1101/359000 ◽

2018 ◽

Author(s):

Gert Pfurtscheller ◽

Andreas Schwerdtfeger ◽

David Fink ◽

Clemens Brunner ◽

Christoph Stefan Aigner ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Middle Cerebral Artery ◽

Frequency Band ◽

Cerebral Artery ◽

Time Delays ◽

Low Frequency ◽

Small Drop ◽

Healthy Individuals ◽

Large Drop

AbstractParticipation in a MRI scan is associated with increased anxiety, thus possibly impacting baseline recording for functional MRI studies. We investigated in 23 healthy individuals without any former MRI experience (scanner-naïve) the relations between anxiety, 0.1-Hz BOLD oscillations and heart rate variability (HRV) in two separate resting state sessions (R1, R2). BOLD signals were recorded from precentral gyrus (PCG) and insula in both hemispheres. Phase-locking and time delays were computed in the frequency band 0.07–0.13 Hz. Positive (pTD) and negative time delays (nTD) were found. The pTD characterize descending neural BOLD oscillations spreading from PCG to insula and nTD characterize ascending vascular BOLD oscillations related to blood flow in the middle cerebral artery. HRV power in two low frequency bands 0.06–0.1 Hz and 0.1–0.14 Hz was computed. Based on the drop rate of the anxiety level from R1 to R2, two groups could be identified: one with a strong anxiety decline (large drop group) and one with a moderate decline or even anxiety increase (small drop group). A significant correlation was found only between the left-hemispheric time delay (pTD, nTD) of BOLD oscillations and anxiety drop, with a dominance of nTD in the large drop group. The analysis of within-scanner HRV revealed a pronounced increase of low frequency power between both resting states, dominant in the band 0.06–0.1 Hz in the large drop group and in the band 0.1–0.14 Hz in the small drop group. These results suggest different mechanisms related to anxiety processing in healthy individuals. One mechanism (large drop group) could embrace an increase of blood circulation in the territory of the left middle cerebral artery (vascular BOLD) and another (small drop group) translates to rhythmic central commands (neural BOLD) in the frequency band 0.1–0.14 Hz.

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Thermoregulation and Heart Rate Variability

Clinical Science ◽

10.1042/cs0900097 ◽

1996 ◽

Vol 90 (2) ◽

pp. 97-103 ◽

Cited By ~ 64

Author(s):

Lee A. Fleisher ◽

Steven M. Frank ◽

Daniel I. Sessler ◽

Christi Cheng ◽

Takashi Matsukawa ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Frequency Band ◽

Low Frequency ◽

Skin Surface ◽

Surface Cooling ◽

Very Low Frequency ◽

Core Cooling ◽

Low Frequency Power ◽

Frequency Power

1. Heart rate variability is modulated by multiple control systems, including autonomic and hormonal systems. Long-term variability, i.e. the very low-frequency band of the power spectra, has been postulated to reflect thermoregulatory vasomotor control, based upon thermal entrainment experiments. However, the relationship between thermoregulatory responses (vasoconstriction and shivering) and heart rate variability has not been studied. 2. We performed two distinct protocols in a series of human subjects. In the first protocol, core temperature was reduced by intravenous infusion of cold saline, while skin temperature was unchanged. The second protocol involved skin-surface warming and cooling until shivering developed. Power spectral analysis was performed using a fast Fourier transformation, and the area in three distinct band-widths was determined. 3. Very low-frequency power (0.0039–0.04 Hz) increased significantly in response to core cooling, peripheral vasoconstriction and shivering, while both very low- and low- (0.04–0.15 Hz) frequency power increased in response to skin-surface cooling. Heart rate decreased during core cooling-induced vasoconstriction, suggesting a direct thermal response, and increased in relation to the metabolic demands associated with shivering. 4. Our results suggest that very low-frequency power is modulated by thermal stimuli which result in core hypothermia and thermoregulatory activity, while skin-surface cooling without core hypothermia does not selectively modulate this frequency band.

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Assessment of ultra low frequency band power of heart rate variability: validation of alternative methods

International Journal of Cardiology ◽

10.1016/s0167-5273(99)00085-6 ◽

1999 ◽

Vol 71 (1) ◽

pp. 1-6 ◽

Cited By ~ 4

Author(s):

Ali R. Bilge ◽

Phyllis K. Stein ◽

Peter P. Domitrovich ◽

Paul L. Gérard ◽

Jeffrey N. Rottman ◽

...

Keyword(s):

Heart Rate ◽

Heart Rate Variability ◽

Frequency Band ◽

Low Frequency ◽

Alternative Methods ◽

Band Power

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Heart Rate Variability, Emotions, and Music

Journal of Psychophysiology ◽

10.1027/0269-8803/a000021 ◽

2010 ◽

Vol 24 (2) ◽

pp. 112-119 ◽

Cited By ~ 9

Author(s):

F. Riganello ◽

A. Candelieri ◽

M. Quintieri ◽

G. Dolce

Keyword(s):

Data Mining ◽

Heart Rate ◽

Heart Rate Variability ◽

Vegetative State ◽

Low Frequency ◽

Emotional Reactions ◽

Heart Beat ◽

Healthy Controls ◽

Frequency Spectra ◽

Emotional Value

The purpose of the study was to identify significant changes in heart rate variability (an emerging descriptor of emotional conditions; HRV) concomitant to complex auditory stimuli with emotional value (music). In healthy controls, traumatic brain injured (TBI) patients, and subjects in the vegetative state (VS) the heart beat was continuously recorded while the subjects were passively listening to each of four music samples of different authorship. The heart rate (parametric and nonparametric) frequency spectra were computed and the spectra descriptors were processed by data-mining procedures. Data-mining sorted the nu_lf (normalized parameter unit of the spectrum low frequency range) as the significant descriptor by which the healthy controls, TBI patients, and VS subjects’ HRV responses to music could be clustered in classes matching those defined by the controls and TBI patients’ subjective reports. These findings promote the potential for HRV to reflect complex emotional stimuli and suggest that residual emotional reactions continue to occur in VS. HRV descriptors and data-mining appear applicable in brain function research in the absence of consciousness.

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