Selected article for: "low predict and machine learning"

Author: Ouyang, Victoria; Ma, Botong; Pignatelli, Niccolo; Sengupta, Shantanu; Sengupta, Partho; Mungulmare, Kunda; Fletcher, Richard Ribón
Title: The use of multi-site photoplethysmography (PPG) as a screening tool for coronary arterial disease and atherosclerosis.
  • Cord-id: z5uvrrbd
  • Document date: 2020_8_7
  • ID: z5uvrrbd
    Snippet: OBJECTIVE 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. METHODS 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 throug
    Document: OBJECTIVE 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. METHODS 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: Coronary Arterial Disease ("CAD"), hypertensive ("Pre-CAD"), and Healthy. MAIN RESULTS PWV values derived from the Ear-Toe measurements increased monotonically as a function of disease severity and age, with median values of 14.2 m/s for the older-patient CAD group, 12.2 m/s for the younger-patient CAD group, 11.6 m/s for the older-patient Pre-CAD group, 10.2 m/s for the younger-patient Pre-CAD group, 9.7 m/s for the older healthy controls, and 8.4 m/s 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 simple low-cost multi-site PPG tool that is powered by a mobile phone and does not require any electrocardiogram (ECG) reference. Furthermore, this method only requires a single anthropometric measurement, which is the patient's height.

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