New principle for measuring arterial blood oxygenation, enabling motion-robust remote monitoring

Abstract

Finger-oximeters are ubiquitously used for patient monitoring in hospitals worldwide. Recently, remote measurement of arterial blood oxygenation (SpO2) with a camera has been demonstrated. Both contact and remote measurements, however, require the subject to remain static for accurate SpO2 values. This is due to the use of the common ratio-of-ratios measurement principle that measures the relative pulsatility at different wavelengths. Since the amplitudes are small, they are easily corrupted by motion-induced variations. We introduce a new principle that allows accurate remote measurements even during significant subject motion. We demonstrate the main advantage of the principle, i.e. that the optimal signature remains the same even when the SNR of the PPG signal drops significantly due to motion or limited measurement area. The evaluation uses recordings with breath-holding events, which induce hypoxemia in healthy moving subjects. The events lead to clinically relevant SpO2 levels in the range 80–100%. The new principle is shown to greatly outperform current remote ratio-of-ratios based methods. The mean-absolute SpO2-error (MAE) is about 2 percentage-points during head movements, where the benchmark method shows a MAE of 24 percentage-points. Consequently, we claim ours to be the first method to reliably measure SpO2 remotely during significant subject motion.

Data-Driven Calibration Estimation for Robust Remote Pulse-Oximetry

“…We demonstrate that, based on a dataset with only small variations in SpO 2 , an accurate calibration model can be determined which outperforms a calibration model based on modelling of the PPG spectrum. Furthermore, we show that the error only slightly increases using solely near-infrared (NIR) wavelengths when compared to the typical combination of visible red and NIR wavelengths, which confirms our earlier observations [8] and widens the scope for camera-based pulse-oximetry, e.g., for sleep or continuous 24/7 monitoring where patients are monitored in full darkness.…”

“…In this paper, we present an empirical, data-driven approach to estimate a calibration model for the indirect APBV method which can be applied to an arbitrary number and selection of wavelengths. This is relevant, as in our earlier paper [8], we showed that adding a third wavelength improves the robustness of the measurement. We demonstrate that, based on a dataset with only small variations in SpO 2 , an accurate calibration model can be determined which outperforms a calibration model based on modelling of the PPG spectrum.…”

“…Over the last few decades, methods have been presented which allow contactless monitoring of human vital signs with regular cameras in both visible and near-infrared conditions based on the detection of visually imperceptible skin color variations caused by blood volume variations. These vital signs include heart rate and derived features [3], respiration [4,5], and, more recently, oxygen saturation [4,[6][7][8][9][10]. Contactless monitoring is especially relevant for: (1) scenarios where direct skin contact should be prevented (e.g., premature infants [11,12] or patients with extensive burns [13]), (2) spot-check scenarios where a reading should be available within seconds, or (3) scenarios where the current diagnostic procedure could affect the clinical outcome (e.g., with polysomnography for sleep monitoring [14][15][16]).…”

“…This is particularly detrimental for measurements where amplitudes have to be extracted from individual PPG waveforms, e.g., for the measurement of peripheral blood oxygen saturation (SpO 2 ). This motivated the development of an indirect method for the estimation of SpO 2 using SpO 2 -dependent pulse signatures [8], which does not require the individual extraction of amplitudes. This method, denoted "APBV" (Adaptive-PBV), showed the feasibility of estimating SpO 2 from distorted PPG signals, e.g., during movement, where the conventional ratio-of-ratios based method renders inaccurate.…”

“…This principle works fine for clean signals, but is rendered inaccurate when the signals are noisy or corrupted by motion artifacts. This motivated the development of the APBV method [8], inspired by the PBV method for camera-based pulse extraction by De Haan et al [17] using the unique spectral signature of the blood volume pulse signal. A short description of the APBV method is provided in the next paragraph.…”