SpO2 Measurement: Non-Idealities and Ways to Improve Estimation Accuracy in Wearable Pulse Oximeters

The blood oxygen saturation level (SpO
₂
) has become one of the vital body parameters for the early detection, monitoring, and tracking of the symptoms of coronavirus diseases 2019 (COVID-19) and is clinically accepted for patient care and diagnostics. Pulse oximetry provides non-invasive SpO
₂
monitoring at home and ICUs without the need of a physician/doctor. However, the accuracy of SpO
₂
estimation in wearable pulse oximeters remains a challenge due to various non-idealities. We propose a method to improve the estimation accuracy by denoising the red and IR signals, detecting the signal quality, and providing feedback to hardware to adjust the signal chain parameters like LED current or transimpedance amplifier gain and enhance the signal quality. SpO
₂
is calculated using the red and infrared photoplethysmogram (PPG) signals acquired from the wrist using Texas Instruments AFE4950EVM. We introduce the green PPG signal as a reference to obtain the window size of the moving average filter for baseline wander removal and as a timing reference for peak and valley detection in the red and infrared PPG signals. We propose the improved peak and valley detection algorithm based on the incremental merge segmentation algorithm. Kurtosis, entropy, and Signal-to-noise ratio (SNR) are used as signal quality parameters, and SNR is further related to the variance in the SpO
₂
measurement. A closed-loop implementation is performed to enhance signal quality based on the signal quality parameters of the recorded PPG signals. The proposed algorithm aims to estimate SpO
₂
with a variance of 1% for the pulse oximetry devices.