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Daily exercise and a healthful diet allows the cardiovascular (CV) system to age slower. However, unhealthy life habits and diseases impact negatively on this condition. In consequence, an individual’s “chronological age” (CHA) may differ from the “arterial age” (AA). There is proven evidence of correlation between arterial stiffness (AS), highly associated with AA, and central blood pressure (BP) waveform, which combined with other CV properties, define a group of characteristic parameters to assess CV Health (CVH). Such assessment, obtained by means of noninvasive and simple arterial measurements is extremely useful for the prevention of CV diseases. Machine Learning (ML) techniques allow the development of predicting models, but usually a large amount of data is required, often unavailable and/or not fully complete. One-dimensional (1D) CV models become an interesting alternative to overcome this issue, since CV system simulations can be performed. OBJECTIVE: A ML model was trained/validated with data from healthy and heterogeneous population (n = 4374), using parameters derived from arterial pressure waveforms (APW) in order to assess CVH. METHODS: Sixteen features were extracted from carotid and radial APW. Such were used for prediction of cardiac output (CO), systemic vascular resistance (SVR), systolic BP (SBPc), diastolic BP (DBPc), carotid-femoral pulse wave velocity (PWVcf), aortic augmentation index (AIa) and CHA as a surrogate of AA. RESULTS: The normalized-RMSEs for CO, SVR, SBPc, DBPc, PWVcf, AIa and CHA using the best-performing model were 5.4%, 2.2%, 0.1%, 0.06%, 0.6%, 1.2% and 5.5%, respectively. CONCLUSION: The acquired results showed that the ML models obtained from measurements in the assessed locations provide an acceptable performance on estimating CVH parameters. Further in-vivo studies are required to validate the application of the outcome.

Recent years have seen a phenomenal change in healthcare paradigms, and Internet of Things (IoT) clubbed with data analytics has been a key player in this field. IoT provides a common platform for seamless exchange between health devices and all the stakeholders of healthcare followed by advanced analysis of the shared pool of data. Clinical decision support system is an important component of the recent eHealth systems which acts as an assistive tool for the medical personnel in getting a deeper insight to patients’ health data and to design more efficient and personalized treatment strategy. Such a fascinating platform of IoT also finds use with respect to CBRNE; especially after a CBRNE event, such eHealth-based decision support system counts significant to guide the medical personnel in treating people. Moreover, having the comprehensive health profile of the mass enables the stakeholders to analyze and identify the potential risk groups, which would enhance the efficiency of treatment and necessary actions.

Introduction: A computer model was developed to estimate the arterial pressure and the cardiac output of humans, by adapting the equations of the Windkessel theory. Material and Methods: The model inputs are based on certain characteristics of patients, such as age, the use of specific drugs and smoking habits. It also predicts the effect of smoking on any of the constituent variables of arterial impedance (Compliance and Resistance) and establishes a statistically significant relationship between them. Results: The outputs of the model are strongly correlated with physiological observations, with a minimum error of 3% for the arterial pressure. Conclusion: The proposed model is useful for providing an easy and low cost estimation of arterial pressure.

Quantification of fluid-structure interactions in arterial walls requires to achieve a complete characterization of flow, shear stress in the interface between blood and endothelium, wall elasticity and wall stresses distribution. Diameter change vs. intravascular pressure is the basis to estimate biomechanical properties (e.g. elasticity, viscosity) of arterial walls in-vivo or in-vitro, being sonomicrometry the gold standard for vessel diameter measurement. High resolution ultrasonography (HRU) is used here in-vitro (as an alternative to sonomicrometry) to get an adequate estimation of the diameter without disturbing the vessel dynamics.