NDLI: A new methodology for Functional Principal Component Analysis from scarce data. Application to stroke rehabilitation

Content Provider	IEEE Xplore Digital Library
Author	Belda-Lois, J.-M. Sanchez-Sanchez, M.L.
Copyright Year	2015
Description	Author affiliation: Inst. de Biomecanica de Valencia, Univ. Politec. de Valencia, Valencia, Spain (Belda-Lois, J.-M.) \|\| Physiotherapy Dept., Univ. of Valencia, Valencia, Spain (Sanchez-Sanchez, M.L.)
Abstract	Functional Principal Component Analysis (FPCA) is an increasingly used methodology for analysis of biomedical data. This methodology aims to obtain Functional Principal Components (FPCs) from Functional Data (time dependent functions). However, in biomedical data, the most common scenario of this analysis is from discrete time values. Standard procedures for FPCA require obtaining the functional data from these discrete values before extracting the FPCs. The problem appears when there are missing values in a non-negligible sample of subjects, especially at the beginning or the end of the study, because this approach can compromise the analysis due to the need to extrapolate or dismiss subjects with missing values. In this paper, we present an alternative methodology extracting the FPCs directly from the sampled data, avoiding the need to have functional data before extracting them. We demonstrate the feasibility of our approach from real data obtained from the analysis of balance recovery after stroke. Finally, we demonstrate that FPCA can obtain differences between groups when these differences are more related to the dynamics of the process than data values at given points.
Starting Page	4602
Ending Page	4605
File Size	669210
Page Count	4
File Format	PDF
ISSN	1557170X
e-ISBN	9781424492718
DOI	10.1109/EMBC.2015.7319419
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-08-25
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Extrapolation Principal component analysis Data analysis Biomedical measurement Market research Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Computer Vision and Pattern Recognition Health Informatics Signal Processing Biomedical Engineering

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