scholarly journals Comparison of Frequency Bands Using Spectral Entropy for Epileptic Seizure Prediction

2013 ◽  
Vol 2013 ◽  
pp. 1-5 ◽  
Author(s):  
Susana Blanco ◽  
Arturo Garay ◽  
Diego Coulombie
Keyword(s):  
Frequency Band ◽  
Epileptic Seizure ◽  
Signal Analysis ◽  
Fourier Spectrum ◽  
Low Frequency ◽  
Eeg Signals ◽  
High Frequencies ◽  
Frequency Bands ◽  
Low Correlation ◽  
Rate Of Increase

Introduction. Under the hypothesis that the uncontrolled neuronal synchronization propagates recruiting more and more neurons, the aim is to detect its onset as early as possible by signal analysis. This synchronization is not noticeable just by looking at the EEG, so mathematical tools are needed for its identification. Objective. The aim of this study is to compare the results of spectral entropies calculated in different frequency bands of the EEG signals to decide which band may be a better tool to predict an epileptic seizure. Materials and Methods. Invasive ictal records were used. We measured the Fourier spectrum entropy of the electroencephalographic signals 4 to 32 minutes before the attack in low, medium and high frequencies. Results. The high-frequency band shows a markedly rate of increase of the entropy, with positive slopes and low correlation coefficient. The entropy rate of growth in the low-frequency band is practically zero, with a correlation around 0.2 and mostly positive slopes. The mid-frequency band showed both positive and negative slopes with low correlation. Conclusions. The entropy in the high frequencies could be predictor, because it shows changes in the previous moments of the attack. Its main problem is the variability, which makes it difficult to set the threshold that ensures an adequate prediction.

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.

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.

Determining patterns of motor recruitment during locomotion

2002 ◽  
Vol 205 (3) ◽  
pp. 359-369 ◽  
Author(s):  
James M. Wakeling ◽  
Motoshi Kaya ◽  
Genevieve K. Temple ◽  
Ian A. Johnston ◽  
Walter Herzog
Keyword(s):  
Motor Unit ◽  
Frequency Band ◽  
Fibre Type ◽  
Low Frequency ◽  
Motor Units ◽  
Motor Unit Recruitment ◽  
Medial Gastrocnemius ◽  
Fibre Types ◽  
Myoelectric Signals ◽  
Frequency Bands

SUMMARY Motor units are the functional units of muscle contraction in vertebrates. Each motor unit comprises muscle fibres of a particular fibre type and can be considered as fast or slow depending on its fibre-type composition. Motor units are typically recruited in a set order, from slow to fast, in response to the force requirements from the muscle. The anatomical separation of fast and slow muscle in fish permits direct recordings from these two fibre types. The frequency spectra from different slow and fast myotomal muscles were measured in the rainbow trout Oncorhynchus mykiss. These two muscle fibre types generated distinct low and high myoelectric frequency bands. The cat paw-shake is an activity that recruits mainly fast muscle. This study showed that the myoelectric signal from the medial gastrocnemius of the cat was concentrated in a high frequency band during paw-shake behaviour. During slow walking, the slow motor units of the medial gastrocnemius are also recruited, and this appeared as increased muscle activity within a low frequency band. Therefore, high and low frequency bands could be distinguished in the myoelectric signals from the cat medial gastrocnemius and probably corresponded, respectively, to fast and slow motor unit recruitment. Myoelectric signals are resolved into time/frequency space using wavelets to demonstrate how patterns of motor unit recruitment can be determined for a range of locomotor activities.

scholarly journals Flicker-noise. Features, diversity and management

2020 ◽  
Vol 2020 (10) ◽  
Author(s):  
A.S. Matsaev
Keyword(s):  
Frequency Band ◽  
Flicker Noise ◽  
Low Frequency ◽  
Frequency Range ◽  
High Frequencies ◽  
Radio Electronics ◽  
Fractional Noises

The article refers to the field of research of noise fluctuations or flicker-noises in electronic amplifiers. The variety of flicker-noise in the energy and frequency dimension is shown, that the manifestation of flicker-noise is not limited to ultra-low frequency range, but under certain conditions can extend to medium and high frequencies. Some areas of radio electronics exposed or using flicker-noise properties are illuminated. It is shown that flicker-noises have a limit level of build-up in accordance with the energy and frequency band forming its thermal and fractional noises. It is shown that for electronic amplifiers flicker-noise is impractical to approximate the simplified ratio of 1/f. Methods of elimination or leveling of flicker-noise are given.

scholarly journals Classification of the Children with ADHD and Healthy Children Based on the Directed Phase Transfer Entropy of EEG Signals

2021 ◽  
Author(s):  
Ali Ekhlasi ◽  
Ali Motie Nasrabadi ◽  
Mohammadreza Mohammadi
Keyword(s):  
Frequency Band ◽  
Phase Transfer ◽  
Effective Connectivity ◽  
Brain Regions ◽  
Transfer Entropy ◽  
Healthy Children ◽  
Eeg Signals ◽  
Children With Adhd ◽  
Frequency Bands ◽  
Artificial Neural Network Ann

Purpose: The present study was conducted to investigate and classify two groups of healthy children and children with Attention Deficit Hyperactivity Disorder (ADHD) by Effective Connectivity (EC) measure. Since early detection of ADHD can make the treatment process more effective, it is important to diagnose it using new methods.   Materials and Methods: For this purpose, Effective Connectivity Matrices (ECMs) were constructed based on Electroencephalography (EEG) signals of 61 children with ADHD and 60 healthy children of the same age. ECMs of each individual were obtained by the directed Phase Transfer Entropy (dPTE) between each pair of electrodes. ECMs were calculated in five frequency bands including, delta, theta, alpha, beta, and gamma. Based on ECM, an Effective Connectivity Vector (ECV) was constructed as a feature vector for the classification process. Furthermore, ECV of different frequency bands was pooled in one global ECV (gECV). Multilayer Artificial Neural Network (ANN) was used in the steps of classification and feature selection by the Genetic Algorithm (GA). Results: The highest classification accuracy with the selected features of ECV was related to theta frequency band with 89.7%. After that, the delta frequency band had the highest accuracy with 89.2%. The results of ANN classification and GA on the gECV reported 89.1% of accuracy. Conclusion: Our findings show that the dPTE measure, which determines effective connectivity between the brain regions, can be used to classify between ADHD and healthy groups. The results of the classification have improved compared to some studies that used the functional connectivity measures.

scholarly journals Modulation of Brain Electroencephalography Oscillations by Electroacupuncture in a Rat Model of Postincisional Pain

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
Jing Wang ◽  
Jing Wang ◽  
Xuezhu Li ◽  
Duan Li ◽  
Xiao-Li Li ◽  
...  
Keyword(s):  
Frequency Band ◽  
Low Frequency ◽  
Spontaneous Pain ◽  
Brain Oscillations ◽  
Beta Band ◽  
Pain Model ◽  
Frequency Bands ◽  
Analgesic Effects ◽  
Before And After ◽  
Beta Frequency

The present study aimed to investigate how ongoing brain rhythmical oscillations changed during the postoperative pain and whether electroacupuncture (EA) regulated these brain oscillations when it relieved pain. We established a postincisional pain model of rats with plantar incision to mimic the clinical pathological pain state, tested the analgesic effects of EA, and recorded electroencephalography (EEG) activities before and after the EA application. By analysis of power spectrum and bicoherence of EEG, we found that in rats with postincisional pain, ongoing activities at the delta-frequency band decreased, while activities at theta-, alpha-, and beta-frequency bands increased. EA treatment on these postincisional pain rats decreased the power at high-frequency bands especially at the beta-frequency band and reversed the enhancement of the cross-frequency coupling strength between the beta band and low-frequency bands. After searching for the PubMed, our study is the first time to describe that brain oscillations are correlated with the processing of spontaneous pain information in postincisional pain model of rats, and EA could regulate these brain rhythmical frequency oscillations, including the power and cross-frequency couplings.

Effect of Spindle Speeds on 3D Topography of Ball-End Milled Surfaces Using Wavelet Analysis Method

2016 ◽  
Vol 836-837 ◽  
pp. 13-19 ◽  
Author(s):  
Shuai Liu ◽  
Jun Zhao ◽  
Wen Zhen Qin ◽  
Ji Ming Pang
Keyword(s):  
Wavelet Analysis ◽  
Frequency Band ◽  
Low Frequency ◽  
Spindle Speed ◽  
Median Frequency ◽  
Frequency Bands ◽  
Multi Scale ◽  
3D Topography ◽  
Changing Trend ◽  
Height Data

Optical profiler is employed to acquire topography height data of ball-end milled die steel surface under different spindle speeds ranging from 2000rpm to 12000rpm with lead angle of 20° and tilt angle of-10°. By multi-scale wavelet analysis, measured height data are decomposed and then been reconstructed, meanwhile 3D topography and 3D roughness in different frequency bands are obtained. The results show that the changing trend of roughness with frequency band under different spindle speeds is not the same. In the high frequency bands, roughness has a tendency to increase with the increasing spindle speed. In the median frequency band, the roughness of the surface machined under low spindle speed 2000 rpm is the largest and the roughness of the surface machined under high spindle speed 12000 rpm is the lowest. In the low frequency bands, the roughness of the surface machined under low spindle speed 2000rpm is much larger than those obtained under other spindle speeds, and with the increasing spindle speed, the changing trend of roughness increases firstly then decreases.

A Wavelet-Based Watermarking Algorithm Using the Human Visual System

2012 ◽  
Vol 424-425 ◽  
pp. 304-308
Author(s):  
Yu Feng Chen ◽  
Gang Yin
Keyword(s):  
Visual System ◽  
Frequency Band ◽  
Human Visual System ◽  
Low Frequency ◽  
Jpeg Compression ◽  
Experimental Results ◽  
Frequency Bands

A wavelet based multiresolution watermarking method using the human visual system (HVS) is proposed. The watermark is added to the large coefficients at the middle frequency bands and low frequency band of the DWT of an image. The experimental results show that the proposed method is robust for some common image distortions, such as cutting, filtering and the JPEG compression

Comparative Feature Analysis of Ground Surface Vibration Induced by High-Speed Train Running on Viaduct and Embankment

2014 ◽  
Vol 638-640 ◽  
pp. 1229-1232
Author(s):  
Kun Ming Mao ◽  
Ting Ting Wang ◽  
Qian Wen Ru ◽  
Yan Li
Keyword(s):  
Frequency Band ◽  
High Frequency ◽  
High Speed ◽  
Fourier Spectrum ◽  
Three Dimensional ◽  
Low Frequency ◽  
Ground Surface ◽  
Vertical Acceleration ◽  
High Frequency Band ◽  
Surface Vibration

Based on the Abaqus parallel computing cluster system platform, the three-dimensional finite element model of train-track-viaduct/embankment-foundation-soil coupling is established. The three-dimensional space-time variation and Fourier spectrums characters of ground surface vibration vertical accelerations by high-speed train running on viaduct and embankment are simulated. The result shows that ground surface vibration is mainly excited by periodic axle force of the train in the site near the viaduct pier. In the site far from the viaduct pier, ground surface vibration is mainly from the transmission of the site near the viaduct pier. With the increased distance between the viaduct pier, the peak value of ground surface vibration vertical acceleration decreases, and decreases significantly when the distance is within 10m. There are two main frequency bands of Fourier spectrum of ground surface vibration vertical acceleration: low-frequency band 0-12Hz and high-frequency band 35-47Hz of viaduct route, and low-frequency band 0-21Hz and high-frequency band 25-45Hz of embankment route. In general, with the increased distance between viaduct/embankment, Fourier spectrum amplitude of every frequency band decrease, and attenuation speed of high-frequency band is much faster than-frequency band’s.

scholarly journals Experimental Study on the Measurement of Water Bottom Vibration Induced by Underwater Drilling Blasting

2015 ◽  
Vol 2015 ◽  
pp. 1-8 ◽  
Author(s):  
Gu Wenbin ◽  
Chen Jianghai ◽  
Wang Zhenxiong ◽  
Wang Zhihua ◽  
Liu Jianqing ◽  
...  
Keyword(s):  
Experimental Study ◽  
Field Experiment ◽  
Frequency Band ◽  
Signal Analysis ◽  
Low Frequency ◽  
Ground Vibration ◽  
Frequency Component ◽  
Vibration Signals ◽  
Attenuation Laws ◽  
Water Bottom

Due to the lack of proper instrumentations and the difficulties in underwater measurements, the studies about water bottom vibration induced by underwater drilling blasting are seldom reported. In order to investigate the propagation and attenuation laws of blasting induced water bottom vibration, a water bottom vibration monitor was developed with consideration of the difficulties in underwater measurements. By means of this equipment, the actual water bottom vibration induced by underwater drilling blasting was measured in a field experiment. It shows that the water bottom vibration monitor could collect vibration signals quite effectively in underwater environments. The followed signal analysis shows that the characteristics of water bottom vibration and land ground vibration induced by the same underwater drilling blasting are quite different due to the different geological environments. The amplitude and frequency band of water bottom vibration both exceed those of land ground vibration. Water bottom vibration is mainly in low-frequency band that induced by blasting impact directly acts on rock. Besides the low-frequency component, land vibration contains another higher frequency band component that induced by followed water hammer wave acts on bank slope.

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