Pulse oximetry based on photoplethysmography imaging with red and green light

Abstract

Remotely measuring the arterial blood oxygen saturation (SpO₂) in visible light (Vis) involves different probing depths, which may compromise calibratibility. This paper assesses the feasibility of calibrating camera-based SpO₂ (SpO_2,cam) using red and green light. Camera-based photoplethysmographic (PPG) signals were measured at 46 healthy adults at center wavelengths of 580 nm (green), 675 nm (red), and 840 nm (near-infrared; NIR). Subjects had their faces recorded during normoxia and hypoxia and under gradual cooling. SpO_2,cam estimates in Vis were based on the normalized ratio of camera-based PPG amplitudes in red over green light (RoG). SpO_2,cam in Vis was validated against contact SpO₂ (reference) and compared with SpO_2,cam estimated using red-NIR wavelengths. An RoG-based calibration curve for SpO₂ was determined based on data with a SpO₂ range of 85–100%. We found an $A^{*}_{rms}$ error of 2.9% (higher than the $A^{*}_{rms}$ for SpO_2,cam in red-NIR). Additional measurements on normoxic subjects under temperature cooling (from $21\,^{\circ }{\text{C}}$ to $<15\,^{\circ }{\text{C}}$) evidenced a significant bias of − 1.7, CI [− 2.7, − 0.7]%. It was also noted that SpO$_{\text{2,cam}}$ estimated at the cheeks was significantly biased (− 3.6, CI [− 5.7, − 1.5]%) with respect to forehead estimations. Under controlled conditions, SpO$_{\text{2,cam}}$ can be calibrated with red and green light but the accuracy is less than that of SpO$_{\text{2,cam}}$ estimated in the usual red-NIR window.

Appendix A: Motion interference cancellation

A core task in cancelling a motion interference signal is its estimation from normalized camera-based PPG signals. To do this, the following principles were explored: 1. the periodicity of the camera-based PPG signal source, s(t); 2. the randomness of motion signals; and, most importantly, 3. the assumption that camera-based PPG and motion artifacts are uncorrelated.

In particular, recognizing that R$_{\text{n}}$(t) and IR$_{\text{n}}$(t) are suitable candidates for the purpose of determining a motion estimate $m_e(t)\approx m(t)$ (see Eqs. 2, 3), we began by coarsely estimating RoIR from the available signals as $RR_0 = |R_n|/|IR_n|$.

The $RR_0$ estimate was refined by finding $\delta _{opt}$ such that $RR(\delta _{opt}) = RR_0 + \delta _{opt}$. To enable this, the principle (3) was translated into to the formulation given by Eq. 11.

$$\begin{aligned} \begin{aligned}&\underset{\delta }{\text {minimize}}&\sum _k|(G_n[k] - m_e[k]) m_e[k]| \\&\text {subject to}&m_e[k] = \frac{R_n[k] - IR_n[k] RR(\delta )}{1-RR(\delta )},&&\\&&RR(\delta ) = RR_0 + \delta ; \;\,\delta \in [-0.15,\,0.15]. \end{aligned} \end{aligned}$$

(11)

with index k being the temporal index. Once $\delta _{opt}$ was available, we estimated the optimal $m_e$ as $\frac{R_n - IR_n RR(\delta _{opt})}{1-RR(\delta _{opt})}$. Minimizing the correlation between the motion-corrected camera-based PPG signal in green, $y[k] = G_n[k] - m_e[k]$, and $m_e[k]$, has resulted in unique solutions for all subjects in the dataset.

However, estimation errors were noticeable in subjects with low SNR. To ameliorate for the issue, signals were processed in consecutive windows of 1500 digital samples. Whenever artifact contamination in a particular window was practically irrelevant and/or indistinguishable from the sensor noise level, no correction was performed. Otherwise, wavelet signal denoising was applied to the final $m_e$ using the Matlab function wdenoise (settings: wavelet type “sym4”, level 4).

Motion interference cancellation was applicable to windows in 19 recordings out of 46 (41% of the data from studies N and H). Figure 11a–c exemplifies R$_{\text{n}}$(t) with its version after motion interference cancellation, R$_{\text{n}}$(t) – m$_{\text{e}}$(t), in representative subjects at which motion cancellation was performed. Overall, results suggest an advantage of cancelling strong motion artifacts but minor gains for the remainder.

Fig. 11

Examination of outcomes of the motion interference cancellation in R$_{\text{n}}$(t) signals. The label “OK” indicates intervals where the signal reconstruction was satisfactory. Conversely, “?” evidences doubtful need for motion interference cancellation or imperfect signal reconstruction