The use of multi-site photoplethysmography (PPG) as a screening tool for coronary arterial disease and atherosclerosis. (29th June 2021)

• Record Type: Journal Article
• Authors: Ouyang, Victoria; Ma, Botong; Pignatelli, Niccolo; Sengupta, Shantanu; Sengupta, Partho; Mungulmare, Kunda; Fletcher, Richard Ribon
• Is Part Of: Physiological measurement. Volume 42:Number 6(2021)
• Publication Date: 2021-06-29
• Subjects: photoplethysmography -- pulse wave velocity -- atherosclerosis -- coronary arterial disease -- cardiovascular -- diagnosis -- mHealth; Physiology -- Measurement -- Periodicals; Patient monitoring -- Periodicals; 612
• DOI: 10.1088/1361-6579/abad48
• Languages: English
• ISSNs: 0967-3334

Abstract

Abstract: Objective . We present the design and validation of a non-invasive smart-phone based screening tool for atherosclerosis and coronary arterial disease (CAD), which is the leading cause of mortality worldwide. Approach . We designed a three-channel photoplethysmography (PPG) device that connects to a smart phone application for measuring pulse transit time (PTT) and pulse wave velocity (PWV) using PPG probes that are simultaneously clipped onto to the ear, index finger, and big toe, respectively. Validation was performed through a clinical study with 100 participants (age 20 to 77) at a research hospital in Nagpur, India. Study subjects were stratified by age and divided into three groups corresponding to the disease severity: CAD, hypertensive ('Pre-CAD'), and Healthy. Main results . PWV measurements derived from the Ear-Toe probe measurements yielded the best performance, with median PWV values increasing monotonically as a function of disease severity and age, as follows: 14.2 m s −1 for the older-patient CAD group, 12.2 m s −1 for the younger-patient CAD group, 11.6 m s −1 for the older-patient Pre-CAD group, 10.2 m s −1 for the younger-patient Pre-CAD group, 9.7 m s −1 for the older healthy controls, and 8.4 m s −1 for the younger healthy controls. Using just two simple features, the PTT and patient height, we demonstrate a machine learning prediction model for CAD with a median accuracy of 0.83 (AUC). Significance . This work demonstrates the ability to predict atherosclerosis and CAD using a single simple physiological measurement with a multi-site PPG tool that is electrically powered by a mobile phone and does not require any electrocardiogram reference. Furthermore, this method only requires a single anthropometric measurement, which is the patient's height.