Studying the clinical significance of ultra-low frequency band power spectra from heart period signal by comparative banded running spectra

2002 ◽  
Author(s):  
X.H. Xu ◽  
Z.X. Xie ◽  
L.C. Chen ◽  
Z.G. Li ◽  
Y.H. Yin ◽  
...  
Keyword(s):  
Clinical Significance ◽  
Frequency Band ◽  
Power Spectra ◽  
Low Frequency ◽  
Heart Period ◽  
Band Power

LFP Power Spectra in V1 Cortex: The Graded Effect of Stimulus Contrast

2005 ◽  
Vol 94 (1) ◽  
pp. 479-490 ◽  
Author(s):  
J. Andrew Henrie ◽  
Robert Shapley
Keyword(s):  
Frequency Band ◽  
Power Spectra ◽  
Low Frequency ◽  
Wide Band ◽  
Gamma Band ◽  
Single Units ◽  
Total Power ◽  
Stimulus Contrast ◽  
Stimulus Response ◽  
Gamma Frequency

We recorded local field potentials (LFPs) and single-unit activity simultaneously in the macaque primary visual cortex (V1) and studied their responses to drifting sinusoidal gratings that were chosen to be “optimal” for the single units. Over all stimulus conditions, the LFP spectra have much greater power in the low-frequency band (≤10 Hz) than higher frequencies and can be described as “1/f.” Analysis of the total power limited to the low, gamma (25–90 Hz), or broad (8–240 Hz) frequency bands of the LFP as a function of stimulus contrast indicates that the LFP power gradually increases with stimulus strength across a wide band in a manner roughly comparable to the increase in the simultaneously recorded spike activity. However, the low-frequency band power remains approximately constant across all stimulus contrasts. More specifically the gamma-band LFP power increases differentially more with respect to baseline than either higher or lower bands as stimulus contrast increases. At the highest stimulus contrasts, we report as others have previously, that the power spectrum of the LFP typically contains an obvious peak in the gamma-frequency band. The gamma-band peak emerges from the overall broadband enhancement in LFP power at stimulus contrasts where most single units' responses have begun to saturate. The temporal/spectral structures of the LFP located in the gamma band—which become most evident at the highest contrasts—provide additional constraints on potential mechanisms underlying the stimulus response properties of spiking neurons in V1.

Heart rate variability and dissociation in panic disorder

2011 ◽  
Vol 26 (S2) ◽  
pp. 147-147
Author(s):  
T. Diveky ◽  
D. Kamaradova ◽  
A. Grambal ◽  
K. Latalova ◽  
J. Prasko ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Panic Disorder ◽  
Frequency Band ◽  
High Frequency ◽  
Power Spectra ◽  
Low Frequency ◽  
Very Low Frequency ◽  
Significant Difference ◽  
Before And After

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

Measuring baroreflex sensitivity from the gain function between arterial pressure and heart period

2002 ◽  
Vol 103 (1) ◽  
pp. 81-88 ◽  
Author(s):  
Gian Domenico PINNA ◽  
Roberto MAESTRI ◽  
Grzegorz RACZAK ◽  
Maria Teresa LA ROVERE
Keyword(s):  
Arterial Pressure ◽  
Frequency Band ◽  
Baroreflex Sensitivity ◽  
Low Frequency ◽  
Systolic Arterial Pressure ◽  
Normal Subjects ◽  
Heart Period ◽  
Gain Function ◽  
Limits Of Agreement ◽  
Spectral Technique

We tested an innovative approach for estimating baroreflex sensitivity (BRS) from the gain function between spontaneous oscillations of systolic arterial pressure (SAP) and heart period (HP). The major goal was to assess the practical implications of abandoning the classical coherence criterion (⩾0.5) as regards measurability of BRS, and agreement with values of BRS obtained using the phenylephrine test (Phe-BRS). We studied 19 normal subjects, 44 patients with a history of previous myocardial infarction (MI) and 45 patients with chronic heart failure (CHF). The experimental protocol included recording of SAP and HP for 10min of supine rest, and evaluation of Phe-BRS. From resting SAP and HP, the gain and coherence functions were computed. The new BRS index was obtained in all subjects by averaging the gain function over the whole low-frequency band (0.04-0.15Hz) (whole-band average BRS, WBA-BRS). WBA-BRS was 7.4 (5.8-10.8)ms/mmHg [median (25th-75th percentile)] in normal controls, 3.1 (1.4-5.4)ms/mmHg in MI patients (P<0.001 compared with normals) and 5.0 (3.2-6.9)ms/mmHg in CHF patients (P<0.01 compared with normals). Using the coherence criterion, BRS could be measured in only 43% and 49% of MI and CHF patients respectively, and the proportion of the low-frequency band contributing to the measurement was 21% (14-47%) and 29% (16-35%) respectively. The correlation between WBA-BRS and Phe-BRS was 0.47, 0.63 and 0.36 in the normal, MI and CHF groups respectively (all P<0.001). The relative bias of WBA-BRS was -5.2ms/mmHg (P<0.001) in normals, -1.4ms/mmHg (P = 0.004) in MI patients and -1.0ms/mmHg (P = 0.11) in CHF patients. The limits of agreement were -13 to 2.6, -7.4 to 4.6 and -9.3 to 7.3ms/mmHg in the normal, MI and CHF groups respectively. Thus the WBA-BRS method standardizes the computation of BRS among subjects, and dramatically increases its measurability in subjects with pathology compared with the classical spectral technique based on the coherence criterion. Compared with Phe-BRS, WBA-BRS tends to give negatively biased results. The correlation and the magnitude of the limits of agreement between the two methods are similar to those observed previously using coherence-based spectral methods.

Evaluating the fracture morphology of shale specimen by the means of AE power spectrum characteristics

2020 ◽  
Author(s):  
Qiang Xie ◽  
Yuxin Ban ◽  
Xiang Fu ◽  
Chunbo He
Keyword(s):  
Acoustic Emission ◽  
Frequency Band ◽  
High Frequency ◽  
Shear Failure ◽  
Power Spectra ◽  
Low Frequency ◽  
Damage Mechanism ◽  
Fracture Morphology ◽  
Dominant Frequency ◽  
Straight Line

&lt;p&gt;Quantitative evaluation of the fracture morphology of shale is an essential prerequisite for assessing the complexity of hydraulic fracturing fracture networks during shale gas exploitation. Brazilian tests coupled with digital image correlation and acoustic emission technique were conducted on black shale in Sichuan Basin in China, the corresponding relationships between the characteristics of the frequency band of acoustic emission power spectra and the micro-damage mechanism of rock specimens were established, and the fracture morphology was quantitatively evaluated. The bedding layer leads to the differences in power spectra characteristics, micro-damage mechanism and fracture morphology of shale. The tension and shear failure of shale matrix induce high-frequency acoustic emission signals, and the tension and shear failure of shale bedding induce low-frequency acoustic emission signals. With the increase of the angle between the bedding layer and loading direction, the dominant frequency and secondary dominant frequency gradually diffuse from low-frequency band to high-frequency band, and the quantitative ratio of high frequency to low frequency&amp;#160; H:L gradually increases. The H:L&amp;#160; of 0&lt;sup&gt;&amp;#176;&lt;/sup&gt; shale specimen is 4.28%: 95.72%, and the fracture is a straight line in shape. The H:L of 30&lt;sup&gt;&amp;#176;&lt;/sup&gt;&amp;#160;and 60&lt;sup&gt;&amp;#176;&lt;/sup&gt; shale specimens are 15.89%: 84.11% and 36.93%: 63.07% respectively, and their fractures are arched in shape. The H:L of 90&lt;sup&gt;&amp;#176; &lt;/sup&gt;specimen is 93.85%: 6.15%, and the fracture is composited arc-straight line in shape. The results can provide references for analyzing micro-seismic data in situ, and provide a theoretical basis for controlling fracture trajectory in hydraulic fracturing in shale reservoirs.&lt;/p&gt;

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

1999 ◽  
Vol 71 (1) ◽  
pp. 1-6 ◽  
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

Contact Current Density Analysis Inside Human Body in Low-Frequency Band Using Geometric Multi-Grid Solver

2020 ◽  
Vol E103.C (11) ◽  
pp. 588-596
Author(s):  
Masamune NOMURA ◽  
Yuki NAKAMURA ◽  
Hiroo TARAO ◽  
Amane TAKEI
Keyword(s):  
Frequency Band ◽  
Current Density ◽  
Human Body ◽  
Low Frequency ◽  
Density Analysis

scholarly journals Spillovers of Stock Markets among the BRICS: New Evidence in Time and Frequency Domains before the Outbreak of COVID-19 Pandemic

2021 ◽  
Vol 14 (3) ◽  
pp. 112
Author(s):  
Kai Shi
Keyword(s):  
Stock Market ◽  
Frequency Band ◽  
Stock Markets ◽  
Emerging Market ◽  
Low Frequency ◽  
Systematic Risk ◽  
Error Variance ◽  
Frequency Bands ◽  
Volatility Spillovers ◽  
Risk Sources

We attempted to comprehensively decode the connectedness among the abbreviation of five emerging market countries (BRICS) stock markets between 1 August 2002 and 31 December 2019 not only in time domain but also in frequency domain. A continuously varying spillover index based on forecasting error variance decomposition within a generalized abbreviation of vector-autoregression (VAR) framework was computed. With the help of spectral representation, heterogeneous frequency responses to shocks were separated into frequency-specific spillovers in five different frequency bands to reveal differentiated linkages among BRICS markets. Rolling sample analyses were introduced to allow for multiple changes during the sample period. It is found that return spillovers dominated by the high frequency band (within 1 week) part declined with the drop of frequencies, while volatility spillovers dominated by the low frequency band (above 1 quarter) part grew with the decline in frequencies; the dynamics of spillovers were influenced by crucial systematic risk events, and some similarities implied in the spillover dynamics in different frequency bands were found. From the perspective of identifying systematic risk sources, China’s stock market and Russia’s stock market, respectively, played an influential role for return spillover and volatility spillover across BRICS markets.

Nonrandom variability in respiratory cycle parameters of humans during stage 2 sleep

1990 ◽  
Vol 69 (2) ◽  
pp. 630-639 ◽  
Author(s):  
M. Modarreszadeh ◽  
E. N. Bruce ◽  
B. Gothe
Keyword(s):  
High Frequency ◽  
Minute Ventilation ◽  
Power Spectra ◽  
Low Frequency ◽  
Neural Mechanism ◽  
Normal Subjects ◽  
Feedback Loops ◽  
Low Frequency Oscillations ◽  
Stage 2 Sleep ◽  
Correlated Components

We analyzed breath-to-breath inspiratory time (TI), expiratory time (TE), inspiratory volume (VI), and minute ventilation (Vm) from 11 normal subjects during stage 2 sleep. The analysis consisted of 1) fitting first- and second-order autoregressive models (AR1 and AR2) and 2) obtaining the power spectra of the data by fast-Fourier transform. For the AR2 model, the only coefficients that were statistically different from zero were the average alpha 1 (a1) for TI, VI, and Vm (a1 = 0.19, 0.29, and 0.15, respectively). However, the power spectra of all parameters often exhibited peaks at low frequency (less than 0.2 cycles/breath) and/or at high frequency (greater than 0.2 cycles/breath), indicative of periodic oscillations. After accounting for the corrupting effects of added oscillations on the a1 estimates, we conclude that 1) breath-to-breath fluctuations of VI, and to a lesser extent TI and Vm, exhibit a first-order autoregressive structure such that fluctuations of each breath are positively correlated with those of immediately preceding breaths and 2) the correlated components of variability in TE are mostly due to discrete high- and/or low-frequency oscillations with no underlying autoregressive structure. We propose that the autoregressive structure of VI, TI, and Vm during spontaneous breathing in stage 2 sleep may reflect either a central neural mechanism or the effects of noise in respiratory chemical feedback loops; the presence of low-frequency oscillations, seen more often in Vm, suggests possible instability in the chemical feedback loops. Mechanisms of high-frequency periodicities, seen more often in TE, are unknown.

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