Wavelet analysis of scaling properties of gastric electrical activity

2006 ◽  
Vol 101 (5) ◽  
pp. 1425-1431 ◽  
Author(s):  
Bruce J. West ◽  
Artur Maciejewski ◽  
Miroslaw Latka ◽  
Tadeusz Sebzda ◽  
Zbigniew Swierczynski ◽  
...  
Keyword(s):  
Time Series ◽  
Systemic Sclerosis ◽  
Surrogate Data ◽  
Long Term Memory ◽  
Wavelet Coefficients ◽  
Healthy Individuals ◽  
Scaling Properties ◽  
Statistical Fluctuations ◽  
Novel Approach ◽  
Scaling Index

We present a novel approach to the analysis of fluctuations in human myoelectrical gastric activity measured noninvasively from the surface of the abdomen. The time intervals between successive maxima of the wavelet transformed quasi-periodic electrogastrographic waveform define the gastric rate variability (GRV) time series. By using the method of average wavelet coefficients, the statistical fluctuations in the GRV signal in healthy individuals are determined to scale in time. Such scaling was previously found in a variety of physiological phenomena, all of which support the hypothesis that physiological dynamics utilize fractal time series. We determine the scaling index in a cohort of 17 healthy individuals to be 0.80 ± 0.14, which compared with a set of surrogate data is found to be significant at the level P < 0.01. We also determined that the dynamical pattern, so evident in the spectrum of average wavelet coefficients of the GRV time series of healthy individuals, is significantly reduced in a cohort of systemic sclerosis patients having a scaling index 0.64 ± 0.17. These results imply that the long-term memory in GRV time series is significantly reduced from healthy individuals to those with systemic sclerosis. Consequently, this disease degrades the complexity of the underlying gastrointestinal control system and this degradation is manifest in the loss of scaling in the GRV time series.

scholarly journals Quantifying Statistical Interdependence by Message Passing on Graphs—Part I: One-Dimensional Point Processes

2009 ◽  
Vol 21 (8) ◽  
pp. 2152-2202 ◽  
Author(s):  
J. Dauwels ◽  
F. Vialatte ◽  
T. Weber ◽  
A. Cichocki
Keyword(s):  
Time Series ◽  
Message Passing ◽  
Point Processes ◽  
Pairwise Alignment ◽  
Surrogate Data ◽  
Map Estimation ◽  
One Dimensional ◽  
Novel Approach ◽  
Average Similarity ◽  
Stochastic Event

We present a novel approach to quantify the statistical interdependence of two time series, referred to as stochastic event synchrony (SES). The first step is to extract “events” from the two given time series. The next step is to try to align events from one time series with events from the other. The better the alignment, the more similar the two time series are considered to be. More precisely, the similarity is quantified by the following parameters: time delay, variance of the timing jitter, fraction of noncoincident events, and average similarity of the aligned events. The pairwise alignment and SES parameters are determined by statistical inference. In particular, the SES parameters are computed by maximum a posteriori (MAP) estimation, and the pairwise alignment is obtained by applying the max-product algorithm. This letter deals with one-dimensional point processes; the extension to multidimensional point processes is considered in a companion letter in this issue. By analyzing surrogate data, we demonstrate that SES is able to quantify both timing precision and event reliability more robustly than classical measures can. As an illustration, neuronal spike data generated by the Morris-Lecar neuron model are considered.

scholarly journals Enhancing Wind Turbine Power Forecast via Convolutional Neural Network

Electronics ◽  
2021 ◽  
Vol 10 (3) ◽  
pp. 261
Author(s):  
Tianyang Liu ◽  
Zunkai Huang ◽  
Li Tian ◽  
Yongxin Zhu ◽  
Hui Wang ◽  
...  
Keyword(s):  
Time Series ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Power ◽  
Rapid Development ◽  
Superior Performance ◽  
Learning Platform ◽  
Novel Approach ◽  
Wind Power Forecast ◽  
Transformation Methods

The rapid development in wind power comes with new technical challenges. Reliable and accurate wind power forecast is of considerable significance to the electricity system’s daily dispatching and production. Traditional forecast methods usually utilize wind speed and turbine parameters as the model inputs. However, they are not sufficient to account for complex weather variability and the various wind turbine features in the real world. Inspired by the excellent performance of convolutional neural networks (CNN) in computer vision, we propose a novel approach to predicting short-term wind power by converting time series into images and exploit a CNN to analyze them. In our approach, we first propose two transformation methods to map wind speed and precipitation data time series into image matrices. After integrating multi-dimensional information and extracting features, we design a novel CNN framework to forecast 24-h wind turbine power. Our method is implemented on the Keras deep learning platform and tested on 10 sets of 3-year wind turbine data from Hangzhou, China. The superior performance of the proposed method is demonstrated through comparisons using state-of-the-art techniques in wind turbine power forecasting.

scholarly journals A Process-Oriented Approach to Identify Evolutions of Sea Surface Temperature Anomalies with a Time-Series of a Raster Dataset

2021 ◽  
Vol 10 (8) ◽  
pp. 500
Author(s):  
Lianwei Li ◽  
Yangfeng Xu ◽  
Cunjin Xue ◽  
Yuxuan Fu ◽  
Yuanyu Zhang
Keyword(s):  
Time Series ◽  
Surface Temperature ◽  
Sea Surface Temperature ◽  
Global Climate ◽  
Sea Surface ◽  
Temperature Anomalies ◽  
Global Climate Changes ◽  
Space And Time ◽  
Novel Approach ◽  
Sea Surface Temperature Anomalies

It is important to consider where, when, and how the evolution of sea surface temperature anomalies (SSTA) plays significant roles in regional or global climate changes. In the comparison of where and when, there is a great challenge in clearly describing how SSTA evolves in space and time. In light of the evolution from generation, through development, and to the dissipation of SSTA, this paper proposes a novel approach to identifying an evolution of SSTA in space and time from a time-series of a raster dataset. This method, called PoAIES, includes three key steps. Firstly, a cluster-based method is enhanced to explore spatiotemporal clusters of SSTA, and each cluster of SSTA at a time snapshot is taken as a snapshot object of SSTA. Secondly, the spatiotemporal topologies of snapshot objects of SSTA at successive time snapshots are used to link snapshot objects of SSTA into an evolution object of SSTA, which is called a process object. Here, a linking threshold is automatically determined according to the overlapped areas of the snapshot objects, and only those snapshot objects that meet the specified linking threshold are linked together into a process object. Thirdly, we use a graph-based model to represent a process object of SSTA. A node represents a snapshot object of SSTA, and an edge represents an evolution between two snapshot objects. Using a number of child nodes from an edge’s parent node and a number of parent nodes from the edge’s child node, a type of edge (an evolution relationship) is identified, which shows its development, splitting, merging, or splitting/merging. Finally, an experiment on a simulated dataset is used to demonstrate the effectiveness and the advantages of PoAIES, and a real dataset of satellite-SSTA is used to verify the rationality of PoAIES with the help of ENSO’s relevant knowledge, which may provide new references for global change research.

scholarly journals Nonlinear analysis of magnetospheric data Part I. Geometric characteristics of the AE index time series and comparison with nonlinear surrogate data

1999 ◽  
Vol 6 (1) ◽  
pp. 51-65 ◽  
Author(s):  
G. P. Pavlos ◽  
M. A. Athanasiu ◽  
D. Kugiumtzis ◽  
N. Hatzigeorgiu ◽  
A. G. Rigas ◽  
...  
Keyword(s):  
Time Series ◽  
State Space ◽  
Null Hypothesis ◽  
Surrogate Data ◽  
Statistical Testing ◽  
Geometrical Parameters ◽  
Geometric Characteristics ◽  
Geometrical Characteristics ◽  
Ae Index ◽  
Index Time Series

Abstract. A long AE index time series is used as a crucial magnetospheric quantity in order to study the underlying dynainics. For this purpose we utilize methods of nonlinear and chaotic analysis of time series. Two basic components of this analysis are the reconstruction of the experimental tiine series state space trajectory of the underlying process and the statistical testing of an null hypothesis. The null hypothesis against which the experimental time series are tested is that the observed AE index signal is generated by a linear stochastic signal possibly perturbed by a static nonlinear distortion. As dis ' ' ating statistics we use geometrical characteristics of the reconstructed state space (Part I, which is the work of this paper) and dynamical characteristics (Part II, which is the work a separate paper), and "nonlinear" surrogate data, generated by two different techniques which can mimic the original (AE index) signal. lie null hypothesis is tested for geometrical characteristics which are the dimension of the reconstructed trajectory and some new geometrical parameters introduced in this work for the efficient discrimination between the nonlinear stochastic surrogate data and the AE index. Finally, the estimated geometric characteristics of the magnetospheric AE index present new evidence about the nonlinear and low dimensional character of the underlying magnetospheric dynamics for the AE index.

The −670G>A polymorphism in the FAS gene promoter region influences the susceptibility to systemic sclerosis

2008 ◽  
Vol 68 (4) ◽  
pp. 584-590 ◽  
Author(s):  
V Liakouli ◽  
M Manetti ◽  
A Pacini ◽  
B Tolusso ◽  
C Fatini ◽  
...  
Keyword(s):  
Systemic Sclerosis ◽  
Topoisomerase I ◽  
Gene Promoter ◽  
Serum Levels ◽  
Scleroderma Renal Crisis ◽  
Genotype Distribution ◽  
Healthy Individuals ◽  
Soluble Fas ◽  
Significant Difference ◽  
Pulmonary Arterial

Objective:To evaluate the role of the single-nucleotide polymorphism (SNP) at position −670 in the FAS gene promoter (FAS−670G>A) in influencing the susceptibility, clinical features and severity of systemic sclerosis (SSc).Methods:350 white Italian SSc patients (259 with limited cutaneous SSc (lcSSc) and 91 with diffuse cutaneous SSc (dcSSc)) and 232 healthy individuals were studied. Patients were assessed for the presence of autoantibodies (anticentromere, anti-topoisomerase I (anti-Scl-70) antibodies), interstitial lung disease (ILD), pulmonary arterial hypertension and scleroderma renal crisis. FAS−670G>A SNP was genotyped by PCR restriction fragment length polymorphism assay. Serum levels of soluble FAS (sFAS) were analysed by ELISA.Results:A significant difference in FAS−670 genotype distribution was observed between SSc patients and healthy individuals (p = 0.001). The frequency of the FAS−670A allele was significantly greater in SSc than in controls (p = 0.001). No significant difference in genotype distribution and allele frequencies was observed between lcSSc and dcSSc, although a greater frequency of the FAS−670A allele was found in dcSSc. The FAS−670AA genotype significantly influenced the predisposition to SSc (OR 1.97, 95% CI 1.35 to 2.88, p = 0.001) and to both lcSSc (OR 1.84, 95% CI 1.23 to 2.75, p = 0.003) and dcSSc (OR 2.37, 95% CI 1.41 to 3.99, p = 0.001). FAS−670A allele frequency was greater, although not significantly, in anti-Scl-70 antibody-positive dcSSc and ILD dcSSc. sFAS was significantly higher in patients and controls carrying the FAS−670AA genotype compared with those carrying the FAS−670GG genotype (p = 0.003 in SSc, p = 0.004 in controls).Conclusion:The FAS−670A allele is significantly associated with susceptibility to SSc, suggesting a role for a genetic control of apoptosis in the pathogenesis of the disease.

Increased Serum Soluble OX40 in Patients with Systemic Sclerosis

2008 ◽  
Vol 35 (12) ◽  
pp. 2359-2362 ◽  
Author(s):  
KAZUHIRO KOMURA ◽  
AYUMI YOSHIZAKI ◽  
MASANARI KODERA ◽  
YOHEI IWATA ◽  
FUMIHIDE OGAWA ◽  
...  
Keyword(s):  
Systemic Lupus Erythematosus ◽  
Systemic Sclerosis ◽  
Lupus Erythematosus ◽  
Tumor Necrosis ◽  
Disease Duration ◽  
Tumor Necrosis Factor Receptor ◽  
Healthy Individuals ◽  
Systemic Lupus ◽  
Clinical Associations ◽  
Necrosis Factor

ObjectiveTo determine levels of serum soluble OX40 (also termed CD134, a member of the tumor necrosis factor receptor superfamily) and their clinical associations in patients with systemic sclerosis (SSc).MethodsSerum soluble OX40 levels were examined by ELISA in 53 patients with SSc, 15 patients with systemic lupus erythematosus (SLE), and 32 healthy individuals.ResultsOX40 levels were significantly elevated in SSc patients (125.7 ± 5.7 pg/ml) compared to patients with SLE (80.7 ± 1.7 pg/ml; p < 0.005) and controls (88.2 ± 3.0 pg/ml; p < 0.0001). Elevated OX40 levels were found to be associated with disease duration of less than 2 years (p < 0.05).ConclusionOur results suggest that serum soluble OX40 levels correlate with the early-onset of SSc disease.

scholarly journals Breakdown coefficients and scaling properties of rain fields

1998 ◽  
Vol 5 (2) ◽  
pp. 93-104 ◽  
Author(s):  
D. Harris ◽  
M. Menabde ◽  
A. Seed ◽  
G. Austin
Keyword(s):  
Time Series ◽  
Parameter Estimation ◽  
Power Law ◽  
Scale Parameter ◽  
Probability Distributions ◽  
Scaling Analysis ◽  
Scaling Properties ◽  
Power Law Exponent ◽  
Scale Similarity ◽  
Parameter Estimation Techniques

Abstract. The theory of scale similarity and breakdown coefficients is applied here to intermittent rainfall data consisting of time series and spatial rain fields. The probability distributions (pdf) of the logarithm of the breakdown coefficients are the principal descriptor used. Rain fields are distinguished as being either multiscaling or multiaffine depending on whether the pdfs of breakdown coefficients are scale similar or scale dependent, respectively. Parameter  estimation techniques are developed which are applicable to both multiscaling and multiaffine fields. The scale parameter (width), σ, of the pdfs of the log-breakdown coefficients is a measure of the intermittency of a field. For multiaffine fields, this scale parameter is found to increase with scale in a power-law fashion consistent with a bounded-cascade picture of rainfall modelling. The resulting power-law exponent, H, is indicative of the smoothness of the field. Some details of breakdown coefficient analysis are addressed and a theoretical link between this analysis and moment scaling analysis is also presented. Breakdown coefficient properties of cascades are also investigated in the context of parameter estimation for modelling purposes.

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