CAMERA-BASED REMOTE PHOTOPLETHYSMOGRAPHY TO PREDICT BLOOD PRESSURE IN CLINIC PATIENTS WITH CARDIOVASCULAR DISEASE

Abstract

Objective: Remote photoplethysmography (rPPG) uses a camera to capture peripheral blood flow. It remains to be seen if rPPG can accurately predict blood pressure (BP). Most studies have included healthy, normotensive subjects. This is the first investigation of rPPG to predict BP for patients with cardiovascular disease (CVD). Design and method: 132 patient visits were completed in a cardiology clinic between March and December, 2022 (Table 1). Patients were seated while a digital camera recorded two 2.5 minute videos of the face and palm. BP was measured by an oscillometric cuff from the left arm at the beginning, midpoint, and end of the session. Continuous heart rate and rhythm were measured by a single-lead ECG from electrodes on the right and left wrists. Continuous PPG was measured by a contact sensor on the left index finger. Videos were cropped and spatially-averaged to extract the green channel, which serves as a proxy for the rPPG signal. Pulse segmentation and selection were used to identify five high-quality consecutive beats. Raw rPPG signal was analyzed with its first and second derivatives into a one-dimensional convolutional neural network. The network is trained to predict systolic and diastolic BP by minimizing the mean squared error between the predicted video-based contactless BP and cuff-based BP measurement. A 5-fold subject independent cross-validation was performed. Results: There is modest correlation between rPPG-predicted and measured systolic BP (r = 0.47, mean error -0.47 ± 15.5 mm Hg), but weaker correlation for diastolic BP (r = 0.16, mean error -0.80 ± 8.1 mm Hg). Conclusions: rPPG shows potential for BP prediction in real-world patients with CVD. Future work will leverage larger data driven machine learning methods to improve the rPPG signal quality and examine the impact on BP measurements. Robust training data, including extreme values of blood pressure and a variety of patient demographics, are needed to improve these models. Major limitations include lack of guideline-directed protocols for rPPG validation for BP prediction and inability to assess temporal correlation between rPPG signal and BP.