Estimating SpO2 via Time-Efficient High-Resolution Harmonics Analysis and Maximum Likelihood Tracking

The accuracy of noninvasive oxygen saturation (SpO2), 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 SpO2 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. The optimized parametric ESPRIT method is applied to improve the accuracy of harmonic power estimation, and the maximum likelihood SpO2 tracking (MLT) technique is proposed to track the most probable uncontaminated harmonic of heart rate frequency. We construct an evaluation platform for testing the proposed method via generated signals and subject tests. Compared with the nonparametric periodogram method, the probability of correct harmonics being found is improved by 18.7% or 19.7%, when the signal is contaminated by motion artifacts or affected by low perfusion, respectively. In comparison with the reference methods, the proposed ESPRIT-MLT method exhibits a lower average root mean square error (RMSE) (1.17%) in the simulation using an MA-contaminated PPG signal, and a lower RMSE (2.70%) in the simulation using an extremely low (0.05%) perfusion index. A comprehensive subject test that consists of 4 activities and 20 subjects shows an average RMSE of 0.84% ($\pm$ 0.44%). Furthermore, the time-efficiency is optimized to be adaptable with wearable devices. Therefore, the proposed method has potential in enhancing the performance of clinical pulse oximetry and wearable SpO2 measurement devices for daily use.