Computation of SpO2 using non-parametric spectral estimation methods from wavelet based motion artifact reduced PPG signals

A pulse oximeter measures the arterial blood oxygen saturation (SpO₂). The common cause of oximeter failure in computing error- free SpO₂ 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 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 computed values of time domain analysis and the Welch 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.