NDLI: Principle component analysis on photoplethysmograms: Blood oxygen saturation estimation and signal segmentation

Content Provider	IEEE Xplore Digital Library
Author	Kejia Li Warren, S.
Copyright Year	2011
Description	Author affiliation: Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USA (Kejia Li; Warren, S.)
Abstract	Most pulse oximeters determine blood oxygen saturation (SpO2) after calculating a coefficient, R, that represents the normalized ratiometric contributions of the pulsatile red and near-infrared photoplethysmograms (PPGs) acquired by the sensor. This paper presents a new approach that uses principle component analysis (PCA) to separate the signal and noise components of unfiltered PPGs and provide the determination of R. Also, rather than use peak-to-valley time intervals to obtain R, this technique uses eigenvalue and eigenvector data obtained during PCA to optimize these time intervals and improve the R calculation. Early analyses on unfiltered PPGs from 16 subjects indicate that these R values compare to those obtained from FFT-based methods and yield SpO2 values consistent with those reported by a commercial unit. All signal data are considered during the PCA process, so this technique shows promise to precisely segment clean versus noise-corrupted PPGs.
Starting Page	7171
Ending Page	7174
File Size	833823
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091812
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Principal component analysis Calibration Noise Blood Optimization Eigenvalues and eigenfunctions Covariance matrix segmentation artifact optimization photoplethysmogram principle component analysis pulse oximetry
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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