A feasibility study on age-related factors of wrist pulse using principal component analysis

2016 ◽  
Author(s):  
Jang-Han Bae ◽  
Young Ju Jeon ◽  
Sanghun Lee ◽  
Jaeuk U. Kim
Keyword(s):  
Principal Component Analysis ◽  
Feasibility Study ◽  
Principal Component ◽  
Component Analysis ◽  
Related Factors ◽  
Age Related

Principal Component Analysis of Working Memory Variables during Child and Adolescent Development

2016 ◽  
Vol 19 ◽  
Author(s):  
Catarina I. Barriga-Paulino ◽  
Elena I. Rodríguez-Martínez ◽  
María Ángeles Rojas-Benjumea ◽  
Carlos M. Gómez
Keyword(s):  
Principal Component Analysis ◽  
Working Memory ◽  
Memory Performance ◽  
Principal Component ◽  
Component Analysis ◽  
Test Battery ◽  
Childhood And Adolescence ◽  
Behavioral Measures ◽  
Age Related ◽  
Speed Accuracy

AbstractCorrelation and Principal Component Analysis (PCA) of behavioral measures from two experimental tasks (Delayed Match-to-Sample and Oddball), and standard scores from a neuropsychological test battery (Working Memory Test Battery for Children) was performed on data from participants between 6–18 years old. The correlation analysis (p < .05) results showed a common maturational trend in working memory performance between these two types of tasks. Applying PCA (Eigenvalues > 1), the scores of the first extracted component were significantly correlated (p < .05) to most behavioral measures, suggesting some commonalities of the processes of age-related changes in the measured variables. The results suggest that this first component would be related to age but also to individual differences during the cognitive maturation process across childhood and adolescence stages. The fourth component would represent the speed-accuracy trade-off phenomenon as it presents loading components with different signs for reaction times and errors.

Age-Related Differences in Gait Kinematics, Kinetics, and Muscle Function: A Principal Component Analysis

2016 ◽  
Vol 45 (3) ◽  
pp. 695-710 ◽  
Author(s):  
Sarah A. Schloemer ◽  
Julie A. Thompson ◽  
Amy Silder ◽  
Darryl G. Thelen ◽  
Robert A. Siston
Keyword(s):  
Principal Component Analysis ◽  
Muscle Function ◽  
Principal Component ◽  
Component Analysis ◽  
Gait Kinematics ◽  
Age Related

Principal Component Analysis Based Dynamic Cone Beam X-Ray Luminescence Computed Tomography: A Feasibility Study

2019 ◽  
Vol 38 (12) ◽  
pp. 2891-2902 ◽  
Author(s):  
Huangsheng Pu ◽  
Peng Gao ◽  
Yang Liu ◽  
Junyan Rong ◽  
Feng Shi ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Principal Component Analysis ◽  
Feasibility Study ◽  
Principal Component ◽  
Component Analysis ◽  
Cone Beam ◽  
X Ray

scholarly journals Investigating the age-related “anterior shift” in the scalp distribution of the P3b component using principal component analysis

2014 ◽  
Vol 51 (7) ◽  
pp. 620-633 ◽  
Author(s):  
Brittany R. Alperin ◽  
Katherine K. Mott ◽  
Dorene M. Rentz ◽  
Phillip J. Holcomb ◽  
Kirk R. Daffner
Keyword(s):  
Principal Component Analysis ◽  
Principal Component ◽  
Component Analysis ◽  
Scalp Distribution ◽  
Age Related

scholarly journals Developmental effects on pattern visual evoked potentials characterized by principal component analysis

2020 ◽  
Author(s):  
Carlyn Patterson Gentile ◽  
Nabin R Joshi ◽  
Kenneth Ciuffreda ◽  
Kristy Arbogast ◽  
Christina Master ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Time Course ◽  
Principal Component ◽  
Component Analysis ◽  
Training Dataset ◽  
Pattern Reversal ◽  
Age Related ◽  
Significant Difference ◽  
Age And Sex ◽  
Visual Evoked

Purpose: Peak amplitude and latency in the pattern reversal visual evoked potential (prVEP) vary with maturation. We considered that principal component analysis (PCA) may be used to describe age-related variation over the entire prVEP time course and provide a means of modeling and removing variation due to developmental age. Methods: prVEP was recorded from 155 healthy subjects ages 11-19 years during two sessions (spaced 0.7 to 17 months apart). We created a model of the prVEP by identifying principal components (PCs) that explained >95% of the variance in a training dataset of 40 subjects. We examined the ability of the PCs to explain variance in an age- and sex-matched test subject group (n=40) and calculated the intra-subject reliability of the PC coefficients between the two sessions. We then explored the effect of subject age and sex upon the PC coefficients. Results: Seven PCs accounted for 96.0% of the variability. The model was generalizable (training vs. test coefficient distributions p>0.36 for all PCs) with good within-subject reliability (R>0.7 for all PCs). The PCA model did not show a significant difference between males and females (F(7,147)=1.69, p=0.12), but showed a significant effect of subject age (F(7,147)=4.37, p=0.0002). Conclusions: PCA is a generalizable, reliable, and unbiased method of analyzing prVEP that can quantify and remove developmental variability present in the global temporal VEP signal. Translational relevance: We describe a novel application of PCA to characterize developmental changes of prVEP in youth that can be used to compare healthy and pathologic pediatric cohorts.

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