In a progressively aging population, it is of utmost importance to develop reliable, non-invasive, and cost-effective tools to estimate biomarkers that can be indicative of cardiovascular risk. Clinical parameters directly measured in the heart or the aorta are crucial for the diagnosis and management of disease. However, their clinical use is severely hampered by their invasive nature, cost, or need for special equipment. Aortic systolic blood pressure (aSBP), cardiac output (CO), end-systolic elastance (Ees), and arterial stiffness indices provide valuable information about the cardiovascular state in humans and are strongly associated with clinical outcomes. This thesis presents original predictive algorithms suitable for estimating such cardiovascular biomarkers from readily available, non-invasive clinical data.

The first aim of this thesis is to develop and validate methods to estimate central hemodynamics. Firstly, an inverse problem-solving method is presented to estimate aSBP and CO from non-invasive measurements of cuff pressure and pulse wave velocity (PWV). The method relies on the adjustment of a previously validated computational arterial tree model. Assessment of the method indicates a high performance on a large human cohort. The second approach involves the machine learning-based estimation of aSBP and CO using again cuff pressure and PWV. Validation of the method on in silico data shows that machine learning offers a greatly accurate alternative for monitoring aSBP and CO.

Additionally, we develop and test a gamut of machine learning frameworks for predicting Ees. First, a machine learning model is trained/tested using as inputs cuff pressure and PWV. The importance of incorporating ejection fraction (EF) as additional input is also assessed. Results indicate that Ees cannot be predicted from pressure-based data alone, while the addition of the EF is indispensable. Alternatively, we propose a novel artificial intelligence-based approach to estimate Ees using the information embedded in clinically relevant systolic timing intervals. Lastly, by means of a deep learning algorithm, we demonstrate that Ees can be accurately predicted using the morphology of the brachial blood pressure waveform.

Furthermore, we aim to improve in vivo assessment of aortic characteristic impedance (Zao) and total arterial compliance (CT). Given that regional PWV measurements are non-invasive and clinically available, we present a non-invasive method for estimating Zao and CT using cuff pressure, cfPWV, and carotid-radial PWV via regression analysis. In silico validation verifies that the method may offer a valuable tool for assessing arterial stiffness while reducing the cost and the complexity of the existing techniques.

As a step forward, we introduce a non-invasive method for estimating CT from a single carotid waveform using artificial neural networks. The proposed methodology is appraised using the large Asklepios human cohort. Precise estimates of CT are yielded, indicating that such an approach could offer promising applications, ranging from fast and cost-efficient hemodynamical monitoring by the physician to integration in wearable technologies.

Finally, in view of the conflicting clinical and experimental evidence regarding the influence of HR on arterial stiffness, we highlight the importance of accounting for the effect of HR-induced blood pressure effects on the PWV measurement.