Computation of SpO<inf>2</inf> using non-parametric spectral estimation methods from wavelet based motion artifact reduced PPG signals

Abstract

A pulse oximeter measures the arterial blood oxygen saturation (SpO 2 ). The common cause of oximeter failure in computing error- free SpO 2 is motion artifact (MA) corruption in the detected PPG signals. In order to have a low failure rate, the pulse oximeters must be provided with a clean artifact-free PPG signals with clearly separable DC and AC parts from which the SpO 2 is computed in time domain. In this paper, we present non-parametric spectral estimation methods for computing SpO 2 . The PPG signals recorded with frequently encountered artifacts (bending, vertical and horizontal motions of finger) were used for validation of the proposed methods. Experimental results revealed that the non-parametric spectral estimation methods are as accurate as the computed values of time domain analysis and the Welch based SpO 2 estimation out performed other non-parametric methods. Further, the Daubechies wavelet based method efficiently reduced motion artifacts restoring all the morphological features of the PPG signals.