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Non-invasive monitoring of key hemodynamical and cardiac parameters using physics-based modelling and artificial intelligence

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Non-invasive monitoring of key hemodynamical and cardiac parameters using physics-based modelling and artificial intelligence

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In vivo application and validation of a novel noninvasive method to estimate the end-systolic elastance

AI-Based Estimation of End-Systolic Elastance From Arm-Pressure and Systolic Time Intervals

Estimation of Left Ventricular End-Systolic Elastance From Brachial Pressure Waveform via Deep Learning

Counting using deep learning regression gives value to ecological surveys

Noninvasive estimation of aortic hemodynamics and cardiac contractility using machine learning

Hemodynamics of ageing, ventricular-arterial coupling and noninvasive assessment of key hemodynamical and cardiac quantities

Implementation and Calibration of a Deep Neural Network to Predict Parameters of Left Ventricular Systolic Function Based on Pulmonary and Systemic Arterial Pressure Signals

Noninvasive Cardiac Output and Central Systolic Pressure From Cuff-Pressure and Pulse Wave Velocity

The impact of heart rate on pulse wave velocity: an in-silico evaluation

Real-Time Cognitive Workload Monitoring Based on Machine Learning Using Physiological Signals in Rescue Missions