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

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.