In clinical practice the ability to monitor arrhythmia episodes in elderly people is helpful to make an accurate diagnosis and choose the proper therapeutic interventions to reduce potential health risk. In this paper we propose an eHealth system to detect atrial fibrillation events as well as provide information about patient’s health status using commercial devices such as a smartphone and a wearable sensor for heart rate monitoring. Our solution consists of a smartphone application able to real time process raw data from the wearable sensor, detect critical events for the patient’s health status, and generate remote alert to medical staff. In the smartphone application a SVM-based algorithm to detect arrhythmia episodes by handling electrocardiogram signal is implemented. To test the performance of the developed eHealth system, the proposed algorithm has been evaluated using acquisitions with atrial fibrillation events. The results show a sensitivity of 94% and a specificity of 93%.

A eHealth System for Atrial Fibrillation Monitoring

Pierleoni, Paola
;
Belli, Alberto
;
Gentili, Andrea;Incipini, Lorenzo;Palma, Lorenzo;Valenti, Simone;Raggiunto, Sara
2018-01-01

Abstract

In clinical practice the ability to monitor arrhythmia episodes in elderly people is helpful to make an accurate diagnosis and choose the proper therapeutic interventions to reduce potential health risk. In this paper we propose an eHealth system to detect atrial fibrillation events as well as provide information about patient’s health status using commercial devices such as a smartphone and a wearable sensor for heart rate monitoring. Our solution consists of a smartphone application able to real time process raw data from the wearable sensor, detect critical events for the patient’s health status, and generate remote alert to medical staff. In the smartphone application a SVM-based algorithm to detect arrhythmia episodes by handling electrocardiogram signal is implemented. To test the performance of the developed eHealth system, the proposed algorithm has been evaluated using acquisitions with atrial fibrillation events. The results show a sensitivity of 94% and a specificity of 93%.
978-3-030-05920-0
978-3-030-05921-7
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11566/264253
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