title: Principle component analysis on photoplethysmograms: blood oxygen saturation estimation and signal segmentation.
abstract: Most pulse oximeters determine blood oxygen saturation (SpO(2)) 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 SpO(2) 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.
authors.affiliation: Department of Electrical & Computer Engineering, Kansas State University, Manhattan, KS 66506, USA. kejiali@ksu.edu
authors.forename: Kejia
authors.initials: K
authors.lastname: Li
authors.forename: Steve
authors.initials: S
authors.lastname: Warren
mesh_terms: D000465:Algorithms; D016477:Artifacts; D002138:Calibration; D006801:Humans; D015233:Models, Statistical; D010092:Oximetry / Q000379:methods; D010100:Oxygen / Q000737:chemistry; D017156:Photoplethysmography / Q000295:instrumentation* / Q000379:methods; D025341:Principal Component Analysis; D012044:Regression Analysis; D015203:Reproducibility of Results; D059629:Signal-To-Noise Ratio; D013997:Time Factors
chemical_list: D010100:Oxygen