Time-dependent frequency domain principal components analysis of multichannel non-stationary signals

2006 ◽  
Vol 50 (9) ◽  
pp. 2339-2360 ◽  
Author(s):  
Hernando Ombao ◽  
Moon-ho Ringo Ho
Keyword(s):  
Frequency Domain ◽  
Principal Components Analysis ◽  
Principal Components ◽  
Time Dependent ◽  
Dependent Frequency ◽  
Stationary Signals ◽  
Components Analysis

Diagnoses Generated by Numerical Taxonomic Methods Applied to Standard Blood Variables

1980 ◽  
Vol 19 (04) ◽  
pp. 205-209
Author(s):  
L. A. Abbott ◽  
J. B. Mitton
Keyword(s):  
Liver Cirrhosis ◽  
Renal Disease ◽  
Principal Components Analysis ◽  
Discriminant Function ◽  
Principal Components ◽  
Discriminant Function Analysis ◽  
Chronic Renal Disease ◽  
Function Analysis ◽  
Infectious Hepatitis ◽  
Components Analysis

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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