Topography of Clonidine-induced Electroencephalographic Changes Evaluated by Principal Component Analysis

2000 ◽  
Vol 92 (6) ◽  
pp. 1545-1552 ◽  
Author(s):  
Petra Bischoff ◽  
Eckehard Scharein ◽  
Gunter N. Schmidt ◽  
Georg von Knobelsdorff ◽  
Burkhart Bromm ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Frequency Band ◽  
Principle Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Fast Fourier Transformation ◽  
Complex Data ◽  
Multivariate Statistical ◽  
Double Blind ◽  
Principle Component

Background Principal component analysis is a multivariate statistical technique to facilitate the evaluation of complex data dimensions. In this study, principle component analysis was used to reduce the large number of variables from multichannel electroencephalographic recordings to a few components describing changes of spatial brain electric activity after intravenous clonidine. Methods Seven healthy volunteers (age, 26 +/- 3 [SD] yr) were included in a double-blind crossover study with intravenous clonidine (1.5 and 3.0 microg/kg). A spontaneous electroencephalogram was recorded by 26 leads and quantified by standard fast Fourier transformation in the delta, theta, alpha, and beta bands. Principle component analysis derived from a correlation matrix calculated between all electroencephalographic leads (26 x 26 leads) separately within each classic frequency band. The basic application level of principle component analysis resulted in components representing clusters of electrodes positions that were differently affected by clonidine. Subjective criteria of drowsiness and anxiety were rated by visual analog scales. Results Topography of clonidine-induced electroencephalographic changes could be attributed to two independent spatial components in each classic frequency band, explaining at least 85% of total variance. The most prominent effects of clonidine were increases in the delta band over centroparietooiccipital areas and decreases in the alpha band over parietooccipital regions. Clonidine administration resulted in subjective drowsiness. Conclusions Data from the current study supported the fact that spatial principle component analysis is a useful multivariate statistical procedure to evaluate significant signal changes from multichannel electroencephalographic recordings and to describe the topography of the effects. The clonidine-related changes seen here were most probably results of its sedative effects.

A strategy for selecting diverse accessions using Principal Component Analysis from a large germplasm collection of soybean

2003 ◽  
Vol 1 (2-3) ◽  
pp. 151-156 ◽  
Author(s):  
R. L Sapra ◽  
S. K. Lal
Keyword(s):  
Principal Component Analysis ◽  
Sample Size ◽  
Principle Component Analysis ◽  
Quantitative Traits ◽  
Germplasm Collection ◽  
Principal Component ◽  
Target Population ◽  
Component Analysis ◽  
Principle Component ◽  
The Individual

AbstractWe suggest a diversity-dependent strategy, based on Principle Component Analysis, for selecting distinct accessions/parents for breeding from a soybean germplasm collection comprising of 463 lines, characterized and evaluated for 10 qualitative and eight quantitative traits. A sample size of six accessions included all the three states, namely low, medium and high of the individual quantitative traits, while a sample of 16–19 accessions included all the 60–64 distinct states of qualitative as well as quantitative traits. Under certain assumptions, the paper also develops an expression for estimating the size of a target population for capturing maximum variability in a sample three accessions.

scholarly journals Support vector regression is an improvement for principle component analysis

2021 ◽  
Vol 23 (06) ◽  
pp. 1699-1715
Author(s):  
Mohamed, A. M. ◽  
◽  
Abdel Latif, S. H ◽  
Alwan, A. S. ◽  
◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Support Vector Regression ◽  
Principle Component Analysis ◽  
Mean Squared Error ◽  
Principal Component ◽  
Component Analysis ◽  
Kernel Functions ◽  
Coefficient Of Determination ◽  
Support Vector ◽  
Principle Component

The principle component analysis is used more frequently as a variables reduction technique. And recently, an evolving group of studies makes use of machine learning regression algorithms to improve the estimation of empirical models. One of the most frequently used machines learning regression models is support vector regression with various kernel functions. However, an ensemble of support vector regression and principal component analysis is also possible. So, this paper aims to investigate the competence of support vector regression techniques after performing principal component analysis to explore the possibility of reducing data and having more accurate estimations. Some new proposals are introduced and the behavior of two different models 𝜀𝜀-SVR and 𝑣𝑣-SVR are compared through an extensive simulation study under four different kernel functions; linear, radial, polynomial, and sigmoid kernel functions, with different sample sizes, ranges from small, moderate to large. The models are compared with their counterparts in terms of coefficient of determination (𝑅𝑅2 ) and root mean squared error (RMSE). The comparative results show that applying SVR after PCA models improve the results in terms of SV numbers between 30% and 60% on average and it can be applied with real data. In addition, the linear kernel function gave the best values rather than other kernel functions and the sigmoid kernel gave the worst values. Under 𝜀𝜀-SVR the results improved which did not happen with 𝑣𝑣-SVR. It is also drawn that, RMSE values decreased with increasing sample size.

Principle Component Analysis Based on New Symmetric Similarity Measures for Heavy-Tailed Data

2018 ◽  
Vol 17 (04) ◽  
pp. 1850029
Author(s):  
Mohammad Seidpisheh ◽  
Adel Mohammadpour
Keyword(s):  
Principal Component Analysis ◽  
Similarity Measure ◽  
Principle Component Analysis ◽  
Similarity Measures ◽  
Principal Component ◽  
Component Analysis ◽  
Principle Component ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Artificial Datasets

We consider the principal component analysis (PCA) for the heavy-tailed distributions. A traditional measure for the classical PCA is the covariance measure. Due to the non-existence of variance of many heavy-tailed distributions, this measure cannot be used for them. We will clarify how to perform PCA in heavy-tailed data by extending a similarity measure based on covariance. We introduce similarity measures based on a new dependence coefficient of heavy-tailed distributions. Using real and artificial datasets, the performance of the proposed PCA is evaluated and compared with the classical one.

Object Classification via PCANet and Color Constancy Model

2014 ◽  
Vol 635-637 ◽  
pp. 997-1000 ◽  
Author(s):  
De Kun Hu ◽  
Li Zhang ◽  
Wei Dong Zhao ◽  
Tao Yan
Keyword(s):  
Principal Component Analysis ◽  
Data Processing ◽  
Functional Properties ◽  
Principle Component Analysis ◽  
Color Constancy ◽  
Principal Component ◽  
Object Classification ◽  
Component Analysis ◽  
Color Images ◽  
Principle Component

In order to classify the objects in nature images, a model with color constancy and principle component analysis network (PCANet) is proposed. The new color constancy model imitates the functional properties of the HVS from the retina to the double-opponent cells in V1. PCANet can be designed and learned extremely, which comprises only the very basic data processing components: cascaded principal component analysis (PCA), binary hashing, and block-wise histograms. At last, a SVM is trained to classify the object in the image. The results of experiments demonstrate the potential of the model for object classification in wild color images.

Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

2021 ◽  
pp. 141-146
Author(s):  
Carlo Cusatelli ◽  
Massimiliano Giacalone ◽  
Eugenia Nissi
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Well Being ◽  
Component Analysis ◽  
Measurement Model ◽  
Statistical Technique ◽  
Multivariate Statistical ◽  
Spatial Principal Component Analysis ◽  
Italian Provinces ◽  
Reflective Measurement

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces.

Multivariate statistical approaches for an early detection of foaming in a refinery SCOT unit

2018 ◽  
Vol 66 (8) ◽  
pp. 665-679
Author(s):  
Hassan Enam Al Mawla ◽  
Andreas Kroll
Keyword(s):  
Principal Component Analysis ◽  
Early Detection ◽  
Time Pressure ◽  
Principal Component ◽  
Component Analysis ◽  
Differential Pressure ◽  
Foam Formation ◽  
Multivariate Statistical ◽  
Statistical Process ◽  
Proper Actions

Abstract The formation of foam in amine units is an issue that plant operators and field personnel are confronted with on a regular basis. The inability to take proper actions in due time may result in plant downtime and increased emissions. Steep rises in differential pressure indicate foam formation, and are monitored manually in practice. Antifoaming agent is added in order to reduce foaming, but this is usually carried out under time pressure. Hence, plant operating authorities have expressed a strong interest in a data-driven solution capable of providing an early warning against foaming. The classical univariate alarm associated with differential pressure can be ineffective for foaming detection due to high misdetection rates and its lateness of detection. Modern univariate approaches based on pattern recognition techniques may not be suitable either for an early detection, as no universally distinctive features of differential pressure are observed prior to foaming in the present study. In this contribution, the multivariate statistical process monitoring approach based on principal component analysis (PCA) is applied to the early detection of foaming in a continuously operated Shell Claus Off-gas Treating (SCOT) unit of a major refinery in Germany. The results are extended to facilitate fully automated and adaptive modeling based on exponentially weighted recursive principal component analysis (EWRPCA).

EVALUATION AND MODELLING OF GROUND WATER QUALITY DATA OF ALLAHABAD CITY BY ENVIRONMETRIC METHODS

2016 ◽  
Vol 2 (4) ◽  
pp. 211
Author(s):  
Girdhari Lal Chaurasia ◽  
Mahesh Kumar Gupta ◽  
Praveen Kumar Tandon
Keyword(s):  
Water Quality ◽  
Principal Component Analysis ◽  
Cluster Analysis ◽  
Factor Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Sampling Location ◽  
Data Sets ◽  
Multivariate Statistical ◽  
Positive Loading

Water is an essential resource for all the organisms, plants and animals including the human beings. It is the backbone for agricultural and industrial sectors and all the small business units. Increase in human population and economic activities have tremendously increased the demand for large-scale suppliers of fresh water for various competing end users.The quality evaluation of water is represented in terms of physical, chemical and Biological parameters. A particular problem in the case of water quality monitoring is the complexity associated with analyzing the large number of measured variables. The data sets contain rich information about the behavior of the water resources. Multivariate statistical approaches allow deriving hidden information from the data sets about the possible influences of the environment on water quality. Classification, modeling and interpretation of monitored data are the most important steps in the assessment of water quality. The application of different multivariate statistical techniques, such as cluster analysis (CA), principal component analysis (PCA) and factor analysis (FA) help to identify important components or factors accounting for most of the variances of a system. In the present study water samples were analyzed for various physicochemical analyses by different methods following the standards of APHA, BIS and WHO and were subjected to further statistical analysis viz. the cluster analysis to understand the similarity and differences among the various sampling stations.  Three clusters were found. Cluster 1 was marked with 3 sampling locations 1, 3 & 5; Cluster-2 was marked with sampling location-2 and cluster-3 was marked with sampling location-4. Principal component analysis/factor analysis is a pattern reorganization technique which is used to assess the correlation between the observations in terms of different factors which are not observable. Observations correlated either positively or negatively, are likely to be affected by the same factors while the observations which are not correlated are influenced by different factors. In our study three factors explained 99.827% of variances. F1 marked  51.619% of total variances, high positive strong loading with TSS, TS, Temp, TDS, phosphate and moderate with electrical conductivity with loading values of 0.986, 0.970, 0.792, 0.744, 0.695,  0.701, respectively. Factor 2 marked 27.236% of the total variance with moderate positive loading with total alkalinity & temp. with loading values 0.723 & 0.606 respectively. It also explained the moderate negative loading with conductivity, TDS, and chloride with loading values -0.698, -0.690, -0.582. Factor F 3 marked 20.972 % of the variances with positive loading with PH, chloride, and phosphate with strong loading of pH 0.872 and moderate positive loading with chloride and phosphate with loading values 0.721, and 0.569 respectively. 

Multivariate Techniques of Use in Biological Research

1969 ◽  
Vol 5 (1) ◽  
pp. 67-77 ◽  
Author(s):  
S. C. Pearce
Keyword(s):  
Principal Component Analysis ◽  
Statistical Methods ◽  
Matrix Algebra ◽  
Principal Component ◽  
Component Analysis ◽  
Canonical Analysis ◽  
Biological Research ◽  
Multivariate Techniques ◽  
Multivariate Statistical Methods ◽  
Multivariate Statistical

SUMMARYMultivariate statistical methods are used increasingly in biological research to investigate the responses of organisms considered as a whole, whereas established statistical methods are usually concerned with measured characteristics considered one at a time. Multivariate techniques are mostly explained in terms of matrix algebra, which is a way of dealing with groups of numbers rather than individual ones. A brief description is given of some elementary results of matrix algebra and a method is presented whereby hypotheses can be generated about interrelations within an organism. Two techniques, principal component analysis and canonical analysis, are described in greater detail. It is emphasized that hypotheses need to be tested even though they have been generated by objective statistical means.

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