A Novel Approach for Motion Artifact Reduction in PPG Signals Based on AS-LMS Adaptive Filter

TLDR: The merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal.

Abstract: The performance of pulse oximeters is highly influenced by motion artifacts (MAs) in photoplethysmographic (PPG) signals. In this paper, we propose a simple and efficient approach based on adaptive step-size least mean squares (AS-LMS) adaptive filter for reducing MA in corrupted PPG signals. The presented method is an extension to our prior work on efficient use of adaptive filters for reduction of MA in PPG signals. The novelty of the method lies in the fact that a synthetic noise reference signal for an adaptive filtering process, representing MA noise, is generated internally from the MA-corrupted PPG signal itself instead of using any additional hardware such as accelerometer or source-detector pair for acquiring noise reference signal. Thus, the generated noise reference signal is then applied to the AS-LMS adaptive filter for artifact removal. While experimental results proved the efficacy of the proposed scheme, the merit of the method is clearly demonstrated using convergence and correlation analysis, thus making it best suitable for present-day pulse oximeters utilizing PPG sensor head with a single pair of source and detector, which does not have any extra hardware meant for capturing noise reference signal. In addition to arterial oxygen saturation estimation, the artifact reduction method facilitated the waveform contour analysis on artifact-reduced PPG, and the conventional parameters were evaluated for assessing the arterial stiffness.