Evaluación preliminar de la Variabilidad de la Frecuencia Cardiaca de Pacientes con Fibrilación Auricular Paroxística y Apnea Obstructiva Barra lateral del artículo

2021 ◽  
Author(s):  
◽  
B. Becerra-Luna
Keyword(s):  
Frequency Band ◽  
Noise Suppression ◽  
Spectral Power ◽  
Low Frequency ◽  
Ecg Signals ◽  
Higher Power ◽  
Regular Sampling ◽  
Respiratory Polygraphy ◽  
Hypopnea Syndrome ◽  
Constant Period

This paper describes the processing of electrocardiographic (ECG) signals from 16 patients diagnosed with paroxysmal atrial fibrillation and sleep apnea-hypopnea syndrome (SAHS) classified as either moderate or severe by respiratory polygraphy. Processing goes from acquisition up to the analysis of their heart rate variability (HRV), where original computer scripts written in MATLAB R2020b are used within scripts adapted from other research groups. Computer processing included linear resampling, noise suppression, R-wave detection, misidentified peaks correction, tachogram resampling at a constant period and trend removal. Regular sampling is mandatory for Fourier analysis through Welch’s periodogram. Once the spectral power was estimated, the HRV was evaluated before, during and after an apnea episode. The behavior of the HRV was compared to the group of patients with moderate SAHS against those with severe SAHS. When comparing the groups at post-apnea stage, significant differences were found in the normalized low-frequency band (LF: 0.04–0.15Hz, p=0.0183), and also in the normalized high frequency band (HF: 0.15–0.4 Hz, p=0.0182), which suggests that in patients with severe SAHS the sympathetic activity is higher (power in LF band), which in turn presupposes that the autonomic nervous system is in frequent alertness, which has been associated with high cardiovascular risk.

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.

Application of complex-trace analysis to seismic data for random-noise suppression and temporal resolution improvement

Geophysics ◽  
2006 ◽  
Vol 71 (3) ◽  
pp. V79-V86 ◽  
Author(s):  
Hakan Karsli ◽  
Derman Dondurur ◽  
Günay Çifçi
Keyword(s):  
Seismic Data ◽  
Trace Analysis ◽  
Noise Suppression ◽  
Single Channel ◽  
Random Noise ◽  
Synthetic Data ◽  
Low Frequency ◽  
Bright Spot ◽  
Phase Information ◽  
Resolution Improvement

Time-dependent amplitude and phase information of stacked seismic data are processed independently using complex trace analysis in order to facilitate interpretation by improving resolution and decreasing random noise. We represent seismic traces using their envelopes and instantaneous phases obtained by the Hilbert transform. The proposed method reduces the amplitudes of the low-frequency components of the envelope, while preserving the phase information. Several tests are performed in order to investigate the behavior of the present method for resolution improvement and noise suppression. Applications on both 1D and 2D synthetic data show that the method is capable of reducing the amplitudes and temporal widths of the side lobes of the input wavelets, and hence, the spectral bandwidth of the input seismic data is enhanced, resulting in an improvement in the signal-to-noise ratio. The bright-spot anomalies observed on the stacked sections become clearer because the output seismic traces have a simplified appearance allowing an easier data interpretation. We recommend applying this simple signal processing for signal enhancement prior to interpretation, especially for single channel and low-fold seismic data.

Locally resonant periodic structures with low-frequency band gaps

2013 ◽  
Vol 114 (3) ◽  
pp. 033532 ◽  
Author(s):  
Zhibao Cheng ◽  
Zhifei Shi ◽  
Y. L. Mo ◽  
Hongjun Xiang
Keyword(s):  
Frequency Band ◽  
Periodic Structures ◽  
Low Frequency ◽  
Band Gaps

Low-frequency swell noise suppression based on U-Net

2020 ◽  
Vol 17 (3) ◽  
pp. 419-431
Author(s):  
Rui-qi Zhang ◽  
Peng Song ◽  
Bao-hua Liu ◽  
Xiao-bo Zhang ◽  
Jun Tan ◽  
...  
Keyword(s):  
Noise Suppression ◽  
Low Frequency

Characteristics of heart rate variability in patients with acute coronary syndrome without ST segment elevation in comparison with clinical and biochemical parameters

2021 ◽  
Vol 29 (3) ◽  
pp. 369-378
Author(s):  
Aleksej A. Nizov ◽  
Aleksej I. Girivenko ◽  
Mihail M. Lapkin ◽  
Aleksej V. Borozdin ◽  
Yana A. Belenikina ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Acute Coronary Syndrome ◽  
Creatine Phosphokinase ◽  
Spectral Power ◽  
Low Frequency ◽  
Heart Rhythm ◽  
St Segment Elevation ◽  
Coronary Syndrome ◽  
St Segment

BACKGROUND: The search for rational methods of primary, secondary, and tertiary prevention of coronary heart disease. To date, there are several publications on heart rate variability in ischemic heart disease. AIM: To study the state of the regulatory systems in the organism of patients with acute coronary syndrome without ST segment elevation based on the heart rhythm, and their relationship with the clinical, biochemical and instrumental parameters of the disease. MATERIALS AND METHODS: The open comparative study included 76 patients (62 men, 14 women) of mean age, 61.0 0.9 years, who were admitted to the Emergency Cardiology Department diagnosed of acute coronary syndrome without ST segment elevation. On admission, cardiointervalometry was performed using Varicard 2.51 apparatus, and a number of clinical and biochemical parameters were evaluated RESULTS: Multiple correlations of parameters of heart rate variability and clinical, biochemical and instrumental parameters were observed. From this, a cluster analysis of cardiointervalometry was performed, thereby stratifying patients into five clusters. Two extreme variants of dysregulation of the heart rhythm correlated with instrumental and laboratory parameters. A marked increase in the activity of the subcortical nerve centers (maximal increase of the spectral power in the very low frequency range with the underlying reduction of SDNN) in cluster 1 was associated with reduction of the left ventricular ejection fraction: cluster 147.0 [40.0; 49.0], cluster 260.0 [58.0; 64.0], cluster 360.0 [52.5; 64.5] % (the data are presented in the form of median and interquartile range; Me [Q25; Q75], p 0,05). Cluster 5 showed significant reduction in SDNN (monotonous rhythm), combined with increased level of creatine phosphokinase (CPC): cluster 5446,0 [186.0; 782.0], cluster 4141.0 [98.0; 204.0] IU/l; Me [Q25; Q75], p 0.05) and MВ-fraction of creatine phosphokinase; cluster 532.0 [15.0; 45.0], 4 cluster 412.0 [9.0; 18.0] IU/l; Me [Q25; Q75], p 0.05). CONCLUSIONS: In patients with acute coronary syndrome without ST segment elevation, cluster analysis of parameters of heart rate variability identified different peculiarities of regulation of the heart rhythm. Pronounced strain of the regulatory systems of the body was found to be associated with signs of severe pathology: the predominance of VLF (spectral power of the curve enveloping a dynamic range of cardiointervals in the very low frequency range) in spectral analysis with an underlying reduced SDNN is characteristic of patients with a reduced ejection fraction, and a monotonous rhythm is characteristic of patients with an increased level of creatine phosphokinase and MB-fraction of creatine phosphokinase.

An Effective Brain Imaging Biomarker for AD and aMCI: ALFF in Slow-5 Frequency Band

2021 ◽  
Vol 18 ◽  
Author(s):  
Luoyu Wang ◽  
Qi Feng ◽  
Mei Wang ◽  
Tingting Zhu ◽  
Enyan Yu ◽  
...  
Keyword(s):  
Brain Imaging ◽  
Frequency Band ◽  
Low Frequency ◽  
Posterior Cingulate Cortex ◽  
Angular Gyrus ◽  
Imaging Biomarker ◽  
Support Vector ◽  
Frequency Bands ◽  
Local Functional ◽  
Left Angular Gyrus

Background: As a potential brain imaging biomarker, amplitude of low frequency fluc-tuation (ALFF) has been used as a feature to distinguish patients with Alzheimer’s disease (AD) and amnestic mild cognitive impairment (aMCI) from normal controls (NC). However, it remains unclear whether the frequency-dependent pattern of ALFF alterations can effectively distinguish the different phases of the disease. Methods: In the present study, 52 AD and 50 aMCI patients were enrolled together with 43 NC in total. The ALFF values were calculated in the following three frequency bands: classical (0.01-0.08 Hz), slow-4 (0.027-0.073 Hz) and slow-5 (0.01-0.027 Hz) for the three different groups. Subsequently, the local functional abnormalities were employed as features to examine the effect of classification among AD, aMCI and NC using a support vector machine (SVM). Results: We found that the among-group differences of ALFF in the different frequency bands were mainly located in the left hippocampus (HP), right HP, bilateral posterior cingulate cortex (PCC) and bilateral precuneus (PCu), left angular gyrus (AG) and left medial prefrontal cortex (mPFC). When the local functional abnormalities were employed as features, we identified that the ALFF in the slow-5 frequency band showed the highest accuracy to distinguish among the three groups. Conclusion: These findings may deepen our understanding of the pathogenesis of AD and suggest that slow-5 frequency band may be helpful to explore the pathogenesis and distinguish the phases of this disease.

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