Pulse wave reflections reflect cardiac afterload and perfusion, which yield valid indicators for
monitoring cardiovascular status. Accurate quantification of pressure wave reflections …

For the past several years, there has been an increasing focus on deep learning methods
applied into computational pulse diagnosis. However, one factor restraining its development …

Introduction: In studies of pulse wave analysis, single-channel sensors only adopt single
temporal pulse signals without spatial information to show pulse-feeling patterns. Multi …

Monitoring the propagation of mechanical cardiac signals throughout the body is crucial for
assessing cardiovascular health. A common drawback of current gold standard methods for …

The pursuit of groundbreaking health care innovations has led to the convergence of
artificial intelligence (AI) and traditional Chinese medicine (TCM), thus marking a new …

Pulse is an important health indicator that includes physiological parameters like heart rate,
rhythm, and vascular elasticity. While deep learning methods for pulse diagnosis have been …

Background: cardiovascular diseases (CVDs) have become the leading causes of death
worldwide. Arterial stiffness and elasticity are important indicators of cardiovascular health …

Objective. Pulse wave velocity (PWV) is a key diagnostic parameter of the cardiovascular
system's state. However, approaches aimed at PWV characterization often suffer from …

The radial artery signal contains extremely rich medical information. However, the change of
its waveform shape is affected by a large number of cardiovascular and visceral conditions …

Cardiovascular health can be assessed from central blood pressure waveform and arterial
stiffness, highly associated with Arterial Age (AA). However, the acquirement of these …