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

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.