Self-calibrated pulse oximetry algorithm based on photon pathlength change and the application in human freedivers

Area of Science:

• Biomedical optics
• Physiological monitoring
• Medical instrumentation

Background:

• Commercial pulse oximeters rely on empirical calibration, limiting accuracy at low arterial oxygen saturation (SpO2) levels.
• Accurate, self-calibrated estimation of SpO2 is crucial for improved patient monitoring and diagnosis.

Purpose of the Study:

• To develop and validate a self-calibrated algorithm for estimating SpO2 across its full range, addressing calibration challenges at low SpO2.
• To design a calibration-free method for SpO2 estimation using human subject data.

Main Methods:

• Developed a self-calibrated algorithm using diffuse optical spectroscopy and the modified Beer-Lambert law (mBLL).
• The algorithm accounts for variations in photon mean pathlength ($$) with SpO2-related absorption changes.
• Validated the algorithm using data from human freedivers, comparing NIRS-derived SpO2 estimates with ground-truth measurements from arterial blood gas analysis.

Main Results:

• The self-calibrated algorithm significantly outperformed the conventional mBLL method with a constant $$ ratio.
• Achieved a mean absolute difference of $$ compared to $$ with the conventional approach, analyzing 23 ground-truth SpO2 data points (41%-100%).

Conclusions:

• The self-calibrated algorithm enables accurate SpO2 estimation by considering spectral shape variations relative to absorption.
• This method provides reliable SpO2 measurements, particularly in subjects experiencing low oxygen levels.