scholarly journals Assessing Velocity and Directionality of Uterine Electrical Activity for Preterm Birth Prediction Using EHG Surface Records

Sensors ◽  
2020 ◽  
Vol 20 (24) ◽  
pp. 7328
Author(s):  
Franc Jager ◽  
Ksenija Geršak ◽  
Paula Vouk ◽  
Žiga Pirnar ◽  
Andreja Trojner-Bregar ◽  
...  
Keyword(s):  
Classification Accuracy ◽  
Cross Correlation ◽  
Vertical Direction ◽  
Sample Entropy ◽  
Lower Percentage ◽  
Maternal Heart Rate ◽  
Conduction Velocities ◽  
Left And Right ◽  
Short Time ◽  
Uterine Electrical Activity

The aim of the present study was to assess the capability of conduction velocity amplitudes and directions of propagation of electrohysterogram (EHG) waves to better distinguish between preterm and term EHG surface records. Using short-time cross-correlation between pairs of bipolar EHG signals (upper and lower, left and right), the conduction velocities and their directions were estimated using preterm and term EHG records of the publicly available Term–Preterm EHG DataSet with Tocogram (TPEHGT DS) and for different frequency bands below and above 1.0 Hz, where contractions and the influence of the maternal heart rate on the uterus, respectively, are expected. No significant or preferred continuous direction of propagation was found in any of the non-contraction (dummy) or contraction intervals; however, on average, a significantly lower percentage of velocity vectors was found in the vertical direction, and significantly higher in the horizontal direction, for preterm dummy intervals above 1.0 Hz. The newly defined features—the percentages of velocities in the vertical and horizontal directions, in combination with the sample entropy of the EHG signal recorded in the vertical direction, obtained from dummy intervals above 1.0 Hz—showed the highest classification accuracy of 86.8% (AUC=90.3%) in distinguishing between preterm and term EHG records of the TPEHGT DS.

Depressor responses to stimulation of sympathetic afferents in monkeys and dogs

1981 ◽  
Vol 240 (1) ◽  
pp. R23-R28 ◽  
Author(s):  
D. R. Kostreva ◽  
F. A. Hopp ◽  
J. P. Kampine
Keyword(s):  
Fiber Type ◽  
Depressor Response ◽  
Stellate Ganglia ◽  
Afferent Fibers ◽  
Pressor Responses ◽  
Conduction Velocities ◽  
C Fiber ◽  
Cervical Vagotomy ◽  
Left And Right ◽  
Stimulation Of

In dogs and monkeys anesthetized with pentobarbital sodium, stimulation of the cut central ends of the stellate cardiac nerve, the left and right anterior ansae subclavia, and the stellate ganglia resulted in a depressor response when stimulating fibers with conduction velocities in the range of 2.5-10 m/s. These afferents are in the A delta-fiber-type range. Pressor responses could be elicited by stimulating afferent fibers with conduction velocities in the range of 0.5-3.0 m/s. These fibers are in the C-fiber-type range. Stimulation of the abdominal sympathetic afferents always resulted in a depressor response regardless of the conduction velocities of the fibers. No changes in heart rate were observed. Bilateral cervical vagotomy did not alter the pressor or depressor responses.

scholarly journals Long-Range Behaviour and Correlation in DFA and DCCA Analysis of Cryptocurrencies

2019 ◽  
Vol 7 (3) ◽  
pp. 51 ◽  
Author(s):  
Natália Costa ◽  
César Silva ◽  
Paulo Ferreira
Keyword(s):  
Time Scales ◽  
Cross Correlation ◽  
Data Availability ◽  
Fluctuation Analysis ◽  
Financial Assets ◽  
Sliding Windows ◽  
Cross Correlation Analysis ◽  
Short Time ◽  
Detrended Fluctuation ◽  
Great Development

In recent years, increasing attention has been devoted to cryptocurrencies, owing to their great development and valorization. In this study, we propose to analyse four of the major cryptocurrencies, based on their market capitalization and data availability: Bitcoin, Ethereum, Ripple, and Litecoin. We apply detrended fluctuation analysis (the regular one and with a sliding windows approach) and detrended cross-correlation analysis and the respective correlation coefficient. We find that Bitcoin and Ripple seem to behave as efficient financial assets, while Ethereum and Litecoin present some evidence of persistence. When correlating Bitcoin with the other cryptocurrencies under analysis, we find that for short time scales, all the cryptocurrencies have statistically significant correlations with Bitcoin, although Ripple has the highest correlations. For higher time scales, Ripple is the only cryptocurrency with significant correlation.

Simulation of Cavity Extension Formation in the Early Ignition Stage Based on a Coal Block Gasification Experiment

2020 ◽  
Vol 142 (6) ◽  
Author(s):  
Lin Xin ◽  
Jian Li ◽  
Jun Xie ◽  
Chao Li ◽  
Limin Han ◽  
...  
Keyword(s):  
Coal Gasification ◽  
Simulation Experiment ◽  
Vertical Direction ◽  
Underground Coal Gasification ◽  
Ignition Point ◽  
Coal Block ◽  
New Type ◽  
High Temperature Area ◽  
Left And Right ◽  
Temperature Area

Abstract Underground coal gasification (UCG) is a highly efficient new type of coal mining technology with broad future prospects. In order to study the cavity extension formation in the early ignition stage of UCG, a block coal scale UCG simulation experiment was carried out. The results show that after the ignition, the temperature above ignition point rose fastest, and the coal combustion interface and high temperature area moved toward to the above of ignition point, while the temperature of the left and right sides of ignition point rose a little slowly. According to the results of dissected block coal, it is indicated that the extension scale in the vertical direction was significantly larger than other directions; the combustion cavity form was an irregular rectangle like a pear. The results of this experiment revealed the cavity extension process from ignition of UCG channels to the formation of cavity, which provided a foundation for the study of extension characteristics of UCG channel in the entire UCG process.

scholarly journals Short-Term Radio-X-Ray Correlations of Cygnus X-1

2004 ◽  
Vol 194 ◽  
pp. 202-202
Author(s):  
T. Gleissner ◽  
J. Wihns ◽  
G. G. Pooley ◽  
M. A. Nowak ◽  
K. Pottschmidt ◽  
...  
Keyword(s):  
White Noise ◽  
Correlation Coefficient ◽  
Time Scales ◽  
Cross Correlation ◽  
Short Term ◽  
X Ray ◽  
Noise Statistics ◽  
Short Time

We analyze simultaneous radio-X-ray data of Cygnus X-l from the Ryle telescope (RT) and RXTE over more than 4 a. We show that apparent correlations on short time scales in the lightcurves of Cyg X-l are probably the coincidental outcome of white noise statistics.As a measure of correlation between radio and X-ray emission, we calculate the maximum cross-correlation coefficient, ccf, of simultaneous radio and X-ray lightcurves, which are rebinned to a resolution of 32 s and smoothed. Every single X-ray lightcurve segment is cross-correlated with the corresponding radio lightcurve, up to a maximum shift Δt = ±10 h.

Estimation of short-time cross-correlation between frequency bands of event related EEG

2006 ◽  
Vol 157 (2) ◽  
pp. 294-302 ◽  
Author(s):  
J. Żygierewicz ◽  
J. Mazurkiewicz ◽  
P.J. Durka ◽  
P.J. Franaszczuk ◽  
N.E. Crone
Keyword(s):  
Cross Correlation ◽  
Frequency Bands ◽  
Short Time

scholarly journals Comparing SAR-Based Short Time-Lag Cross Correlation and Doppler-Derived Sea Ice Drift Velocities

2018 ◽  
Vol 56 (4) ◽  
pp. 1898-1908 ◽  
Author(s):  
Thomas Kramer ◽  
Harald Johnsen ◽  
Camilla Brekke ◽  
Geir Engen
Keyword(s):  
Sea Ice ◽  
Cross Correlation ◽  
Time Lag ◽  
Ice Drift ◽  
Sea Ice Drift ◽  
Short Time

scholarly journals Application of Subspace-Based Detection Algorithm to Infrasound Signals in Volcanic Areas

2021 ◽  
Vol 8 ◽  
Author(s):  
Mariangela Sciotto ◽  
Placido Montalto
Keyword(s):  
Cross Correlation ◽  
Confusion Matrix ◽  
Detection Threshold ◽  
Detection Algorithm ◽  
Volcano Monitoring ◽  
Signal Subspace ◽  
Long Time ◽  
Time Average ◽  
Short Time ◽  
Correlation Detector

Infrasonic signals investigation plays a fundamental role for both volcano monitoring purpose and the study of the explosion dynamics. Proper and reliable detection of weak signals is a critical issue in active volcano monitoring. In particular, in volcanic acoustics, it has direct consequences in pinpointing the real number of generated events (amplitude transients), especially when they exhibit low amplitude, are close in time to each other, and/or multiple sources exist. To accomplish this task, several algorithms have been proposed in literature; in particular, to overcome limitations of classical approaches such as short-time average/long-time average and cross-correlation detector, in this paper a subspace-based detection technique has been implemented. Results obtained by applying subspace detector on real infrasound data highlight that this method allows sensitive detection of lower energy events. This method is based on a projection of a sliding window of signal buffer onto a signal subspace that spans a collection of reference signals, representing similar waveforms from a particular infrasound source. A critical point is related to subspace design. Here, an empirical procedure has been applied to build the signal subspace from a set of reference waveforms (templates). In addition, in order to determine detectors parameters, such as subspace dimension and detection threshold, even in presence of overlapped noise such as infrasonic tremor, a statistical analysis of noise has been carried out. Finally, the subspace detector reliability and performance, have been assessed by performing a comparison among subspace approach, cross-correlation detector and short-time average/long-time average detector. The obtained confusion matrix and extrapolated performance indices have demonstrated the potentiality, the advantages and drawbacks of the subspace method in tracking volcanic activity producing infrasound events. This method revealed to be a good compromise in detecting low-energy and very close in time events recorded during Strombolian activity.

scholarly journals A New Support Vector Machine Based on Convolution Product

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-19
Author(s):  
Wei-Chang Yeh ◽  
Yunzhi Jiang ◽  
Shi-Yi Tan ◽  
Chih-Yen Yeh
Keyword(s):  
Neural Networks ◽  
Support Vector Machine ◽  
Classification Accuracy ◽  
Numerical Experiments ◽  
Classification Problem ◽  
Support Vector ◽  
Convolution Product ◽  
Swarm Optimization ◽  
New Information ◽  
Short Time

The support vector machine (SVM) and deep learning (e.g., convolutional neural networks (CNNs)) are the two most famous algorithms in small and big data, respectively. Nonetheless, smaller datasets may be very important, costly, and not easy to obtain in a short time. This paper proposes a novel convolutional SVM (CSVM) that has the advantages of both CNN and SVM to improve the accuracy and effectiveness of mining smaller datasets. The proposed CSVM adapts the convolution product from CNN to learn new information hidden deeply in the datasets. In addition, it uses a modified simplified swarm optimization (SSO) to help train the CSVM to update classifiers, and then the traditional SVM is implemented as the fitness for the SSO to estimate the accuracy. To evaluate the performance of the proposed CSVM, experiments were conducted to test five well-known benchmark databases for the classification problem. Numerical experiments compared favorably with those obtained using SVM, 3-layer artificial NN (ANN), and 4-layer ANN. The results of these experiments verify that the proposed CSVM with the proposed SSO can effectively increase classification accuracy.

The multiscale analysis between stock market time series

2015 ◽  
Vol 26 (06) ◽  
pp. 1550071 ◽  
Author(s):  
Wenbin Shi ◽  
Pengjian Shang
Keyword(s):  
Time Series ◽  
Stock Market ◽  
Correlation Coefficient ◽  
Multiscale Analysis ◽  
Cross Correlation ◽  
Multiple Scales ◽  
Financial Time Series ◽  
Sample Entropy ◽  
Financial Time ◽  
Cross Correlation Analysis

This paper is devoted to multiscale cross-correlation analysis on stock market time series, where multiscale DCCA cross-correlation coefficient as well as multiscale cross-sample entropy (MSCE) is applied. Multiscale DCCA cross-correlation coefficient is a realization of DCCA cross-correlation coefficient on multiple scales. The results of this method present a good scaling characterization. More significantly, this method is able to group stock markets by areas. Compared to multiscale DCCA cross-correlation coefficient, MSCE presents a more remarkable scaling characterization and the value of each log return of financial time series decreases with the increasing of scale factor. But the results of grouping is not as good as multiscale DCCA cross-correlation coefficient.

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