Infant EEG Spectral Coherence Data during Quiet Sleep: Unrestricted Principal Components Analysis — Relation of Factors to Gestational Age, Medical Risk, and Neurobehavioral Status

2003 ◽  
Vol 34 (2) ◽  
pp. 54-69 ◽  
Author(s):  
Frank H. Duffy ◽  
Heidelise Als ◽  
Gloria B. McAnulty

EEG spectral coherence data in quiet sleep of 312 infants were evaluated, at 42 weeks post-menstrual age. All were medically healthy and living at home by time of evaluation. The sample consisted of prematurely born infants with a wide spectrum of underlying risk factors, as well as healthy fullterm infants. Initial 3040 coherence variables were reduced by principal components analysis in an unrestricted manner, which avoided the folding of spectral and spatial information into among-subject variance. One hundred fifty factors explained 90% of the total variance; 40 Varimax rotated factors explained 65% of the variance yielding a 50:1 data reduction. Factor loading patterns ranged from multiple spectral bands for a single electrode pair to multiple electrode pairs for a single spectral band and all intermediate possibilities. Simple left-right and anterior-posterior pairings were not observed within the factor loadings. By multiple regression analysis, the 40 factors significantly predicted gestational age at birth. By canonical correlation, significant relationships were demonstrated between the coherence factors and medical risk factors as well as neurobehavioral factors. Using discriminant analysis, the coherence factors successfully discriminated between infants with high and low medical risk status and between those with the best and worst neurobehavioral status. The two factors accounting for the most variance, and chosen across several analyses, indicated increased left central-temporal coherence from 6–24 Hz, and increased frontal-occipital coherence at 10 Hz, for the infants born closest to term with lowest medical risk factors and best neurobehavioral performance.

Author(s):  
Isabel Brusca ◽  
Jorge Olmo ◽  
Margarita Labrador

This chapter aims to analyze the characteristics of Spanish governments that fail to achieve budgetary stability, as well as propose a model for the analysis of financial sustainability of governments that can help in predicting risk for financial sustainability. The analysis is focused on Spanish local governments with more than 5,000 inhabitants that have elaborated the annual plan because they did not achieve budgetary stability (79 local governments). Using the principal components analysis, the authors developed a model for the analysis of the characteristics of these governments. The model is made up of three components created from six indicators usually considered in the literature as relevant. The results evidence the indicators useful to measure the three dimensions identified by the IPSASB as relevant: revenue dimension, debt dimension, and public services dimension.


Author(s):  
Shalini Sahay ◽  
Manju Sharma ◽  
Devendra Kumar ◽  
Bhawar Singh Meena

Background: As biometric indices are difficult to obtain or show gross discrepancies with each other or with gestational age in the late trimester, so for accurate dating additional parameter is required. Fetal kidney length is easy to measure and appear reliable in previous studies.Methods: Well dated 121 antenatal women from 28 to 40 weeks of gestation with no obstetric or medical risk factors were recruited and kidney length is assessed in longitudinal scan and maximum length is taken. fetal kidney length derived gestational age compared with other biometrics indices gestational age.Results: Mean kidney length showed significant correlation (r=0.899, p<0.001 value) with increasing gestational age. Mean kidney length dated pregnancy within ±8.5 days.Conclusions: Fetal kidney length measurement correlated well with other routinely used parameters for the estimation of gestational age and can be used as an accurate parameter to date pregnancy in the late trimester.


1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton

Data taken from the blood of 262 patients diagnosed for malabsorption, elective cholecystectomy, acute cholecystitis, infectious hepatitis, liver cirrhosis, or chronic renal disease were analyzed with three numerical taxonomy (NT) methods : cluster analysis, principal components analysis, and discriminant function analysis. Principal components analysis revealed discrete clusters of patients suffering from chronic renal disease, liver cirrhosis, and infectious hepatitis, which could be displayed by NT clustering as well as by plotting, but other disease groups were poorly defined. Sharper resolution of the same disease groups was attained by discriminant function analysis.


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