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EnglishThe 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 / Arpaia, P.; Cuocolo, R.; Donnarumma, F.; D'Andrea, D.; Esposito, A.; Moccaldi, N.; Natalizio, A.; Prevete, R.. - ELETTRONICO. - (2019), pp. 263-267. ((Intervento presentato al convegno 2nd IEEE International Workshop on Metrology for Industry 4.0 and IoT, MetroInd 4.0 and IoT 2019 tenutosi a ita nel 2019.
| Titolo: | Feasibility of cardiovascular risk assessment through non-invasive measurements |
| Autori: | |
| Data di pubblicazione: | 2019 |
| 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. |
| ISBN: | 9781728104294 |
| Appare nelle tipologie: | 4.1 Contributo in Atti di convegno |
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| MetroIND__Indago.pdf | 2. Post-print / Author's Accepted Manuscript | PUBBLICO - Tutti i diritti riservati | Visibile a tuttiVisualizza/Apri | |
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http://hdl.handle.net/11583/2848088
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