Kalman Filter Based Adaptive Reduction Of Motion Artifact From Photoplethysmographic Signal
Generate an AI Snapshot to get a quick, structured summary of this paper.
A concise AI-generated summary of the paper will appear here once you click Generate AI Snapshot.
TL;DR
Simulation results show acceptable performance regarding LMS and variable step LMS, thus establishing the efficacy of the proposed method, Kalman Filter.
Abstract
Artifact free photoplethysmographic (PPG) signals are necessary for non-invasive estimation of oxygen saturation (SpO2) in arterial blood. Movement of a patient corrupts the PPGs with motion artifacts, resulting in large errors in the computation of Sp02. This paper presents a study on using Kalman Filter in an innovative way by modeling both the Artillery Blood Pressure (ABP) and the unwanted signal, additive motion artifact, to reduce motion artifacts from corrupted PPG signals. Simulation results show acceptable performance regarding LMS and variable step LMS, thus establishing the efficacy of the proposed method.