Paper Info
Title
Feasibility of cardiovascular risk assessment through non-invasive measurements
Abstract
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.
Year
DOI
Venue
2019
10.1109/METROI4.2019.8792909
2019 II Workshop on Metrology for Industry 4.0 and IoT (MetroInd4.0&IoT)
Keywords
Field
DocType
Random Forest classifier,preexisting dataset,electrocardiogram,patients health status,wearable sensors,machine learning algorithm,patient interview,clinic data,body temperature,blood oxygenation,heart rate,heart functionality,wearable system,noninvasive measurements,cardiovascular risk assessment
Patient interview,Wearable computer,Computer science,Risk assessment,Feature extraction,Prediction algorithms,Artificial intelligence,Random forest,Electrocardiography,Machine learning
Conference
ISBN
Citations 
PageRank 
978-1-7281-0430-0
0
0.34
References 
Authors
6
8
Name
Order
Citations
PageRank
Pasquale Arpaia19529.77
Renato Cuocolo200.34
Francesco Donnarumma390.80
Dario D'Andrea400.34
Antonio Esposito500.68
Nicola Moccaldi601.35
Angela Natalizio700.34
Roberto Prevete800.68