Use of spectral estimation methods for computation of SpO2 from artifact reduced PPG signals

Abstract

Arterial blood oxygen saturation (SpO2) is effectively measured by the pulse oximeter. The common cause of pulse oximeter failure, in error- free SpO2 estimation, is motion artifact (MA) corruption in the detected PPG signals. For a reliable and a low failure rate SpO2 estimation, the pulse oximeters must be provided with a clean artifact-free PPG signals with clearly separable DC and AC parts from which the SpO2 is computed in time domain. In this paper, we present non-parametric spectral estimation methods for computing SpO2. 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 time domain analysis and particularly the Blackman-Tukey based SpO2 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.