The present work is a first step in building a wearable system to monitor the heart functionality of a patient and assess the cardiovascular risk by means of non-invasive measurements, such as electrocardiogram (ECG), heart rate, blood oxygenation, and body temperature. Also clinic data obtained by means of a patient interview are taken into account. In this feasibility study, measures from a pre-existing dataset are exploited. They are processed with a machine learning algorithm. Features are first extracted from the measures collected with the wearable sensors. Then, these features are employed together with clinic data to classify the patients health status. A Random Forest classifier was employed and the algorithm was characterized considering different setups. The best accuracy resulted equal to 78.6% in distinguishing three classes of patients, namely healthy, unhealthy non-critical, and unhealthy critical patients.
Feasibility of cardiovascular risk assessment through non-invasive measurements
Contributo in atti di convegno
IEEE International Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)., pp. 263–267, Neaples, Italy, 4-6 June 2019
info:cnr-pdr/source/autori:Arpaia, Pasquale and Cuocolo, Renato and Donnarumma, Francesco and D'Andrea, Dario and Esposito, Antonio and Moccaldi, Nicola and Natalizio, Angela and Prevete, Roberto/congresso_nome:IEEE International Workshop on Metrology for