Characterization and adaptive filtering of motion artifacts in pulse oximetry using accelerometers

Noise, in the form of motion artifact, often leads to false information and acts as a limiting factor in the analysis of pulse oximetric signals. We propose an adaptive filter for filtering motion artifacts from pulse oximetry signals, with accelerometer signals as noise references. We study two adaptive filtering schemes: (1) single-axis and (2) dual-axes stress tests; and apply both the LMS and RLS algorithms to each scheme to compare their effectiveness. Results show that a single-axis adaptive filter employing the RLS algorithm (N=32 and /spl lambda/=0.9999) is adequate to minimize motion artifact.