Estimating SpO via Time-Efficient High-Resolution Harmonics Analysis and Maximum Likelihood Tracking.

The accuracy of noninvasive oxygen saturation (SpO), which is defined by the measurements based on photoplethysmographic (PPG) signals, is intensively affected by motion artifacts (MAs) and low perfusion. This study introduces a novel approach called ESPRIT-MLT to measure SpO when such interferences are present. In contrast to previous studies, the work focuses on the harmonic model of the PPG signal and the probability model of results from harmonic analysis.

A motion-tolerant approach for monitoring SpO and heart rate using photoplethysmography signal with dual frame length processing and multi-classifier fusion.

Monitoring pulse oxygen saturation (SpO) and heart rate (HR) using photoplethysmography (PPG) signal contaminated by a motion artifact (MA) remains a difficult problem, especially when the oximeter is not equipped with a 3-axis accelerometer for adaptive noise cancellation. In this paper, we report a pioneering investigation on the impact of altering the frame length of Molgedey and Schuster independent component analysis (ICAMS) on performance, design a multi-classifier fusion strategy for selecting the PPG correlated signal component, and propose a novel approach to extract SpO and HR readings from PPG signal contaminated by strong MA interference. The algorithm comprises multiple stages, including dual frame length ICAMS, a multi-classifier-based PPG correlated component selector, line spectral analysis, tree-based HR monitoring, and post-processing.