Feasibility of cardiovascular risk assessment through non-invasive measurements

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.

Tipo Pubblicazione: 
Contributo in atti di convegno
Author or Creator: 
Arpaia
Pasquale
Cuocolo
Renato
Donnarumma
Francesco
D'Andrea
Dario
Esposito
Antonio
Moccaldi
Nicola
Natalizio
Angela
Prevete
Roberto
Source: 
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
Date: 
2019
Resource Identifier: 
http://www.cnr.it/prodotto/i/432461
https://ieeexplore.ieee.org/document/8792909
urn:isbn:978-1-7281-0429-4
Language: 
Eng
ISTC Author: