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Advisor(s)
Abstract(s)
Cardiac arrhythmias are disorders that affect the rate and/or rhythm of the heartbeats. The diagnosis of most
arrhythmias is made through the analysis of the electrocardiogram (ECG), which consists of a graphical
representation of the electrical activity of the heart. Atrial fibrillation (AF) is the most present type of
arrhythmia in the world population. In this context, this work deals with the implementation of a system for
automatic analysis of ECG signals aiming to identify AF episodes. The system consists of a signal acquisition
step performed by an ECG sensor connected to an acquisition platform. The acquired signal is transmitted via
bluetooth to a smartphone with Android™ operating system. The signal processing is carried out through an
application developed using the IDE Android™ Studio. When assessed over signals from the MIT-BIH Atrial
Fibrillation database, the R-wave peak detection algorithm showed mean values of sensitivity and positive
predictivity of 98.99% and 95.95%, respectively. The classification model used is based on a long short-term
memory (LSTM) neural network and had an average accuracy of 94.94% for identifying AF episodes.
Description
Keywords
Android Atrial fibrillation BITalino ECG Smartphone
Citation
Borghi, Pedro; Kuhn, Eduardo; Teixeira, João Paulo; Lazaretti, Gabriel (2022). Android-based ECG monitoring system for atrial fibrillation detection using a BITalino® ECG sensor. In Proceedings of the 15th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2022). Vol. 1 p. 177-184. ISBN 978-989-758-552-4
Publisher
SCITEPRESS