Jugular Venous Height Measurement Through Contactless Monitoring

Abstract

Heart failure remains the leading cause of mortality worldwide, underscoring the urgent need for early diagnosis to improve patient outcomes and alleviate the burden on healthcare systems. One of the key indicators of heart failure is elevated Jugular Venous Pressure (JVP), which reflects increased venous pressure and fluid retention when the heart fails to pump blood effectively. Traditional methods of measuring JVP are often time-consuming and expensive. However, the advent of smartphones equipped with high-quality cameras and advanced processing units present an opportunity for a more efficient and cost-effective approach. This research proposes a novel framework utilizing machine learning and signal processing techniques to measure the JVP height in the neck through smartphone video recordings. The framework enables patients to capture and assess their JVP height by simply using their smartphones, offering a practical alternative to conventional methods. To evaluate the effectiveness of the proposed system, a dataset comprising videos of patients recorded from various angles and distances at a cardiopulmonary clinic was utilized. The algorithm’s performance in detecting JVP height was compared with assessments made by medical professionals using ultrasonic methods. The results demonstrate that the proposed system is comparable in terms of accuracy in JVP height measurement with expert’s evaluations. This advancement suggests a promising solution for improving the accessibility and efficiency of heart failure diagnostics, potentially transforming patient self-monitoring and reducing healthcare costs.