Authors:
Gennaro Laudato
1
;
Giovanni Rosa
1
;
Giovanni Capobianco
1
;
Angela Rita Colavita
2
;
Arianna Dal Forno
1
;
Fabio Divino
1
;
Claudio Lupi
1
;
Remo Pareschi
1
;
Stefano Ricciardi
1
;
Luca Romagnoli
1
;
Simone Scalabrino
1
;
Cecilia Tomassini
1
and
Rocco Oliveto
1
Affiliations:
1
STAKE Lab, University of Molise, Pesche (IS), Italy
;
2
ASREM – Regione Molise, Italy
Keyword(s):
Recommender System, Deep Learning, ECG Analysis, Atrial Fibrillation, Arrhythmia.
Abstract:
Atrial fibrillation (AF) is a medical disorder that affects the atria of the heart. AF has emerged as a worldwide cardiovascular epidemic affecting more than 33 million people around the world. Several automated approaches based on the analysis of the ECG have been proposed to facilitate the manual identification of AF episodes. Especially, such approaches analyze the heartbeat morphology (absence of P-wave) or the heart rate (presence of arrhythmia). In this article, we present AMELIA (AutoMatic dEtection of atriaL fIbrillation for heAlthcare), an approach that simulates the doctor’s behavior by considering both the sources of information in a combined way. AMELIA is basically composed of two components; one integrating a LSTM (Long Short-Term Memory) Recurrent Neural Network (RNN) and the second integrating a rhythm analyzer. When the RNN reveals a heartbeat with abnormal morphology, the rhythm analyzer is activated to verify whether or not there is a simultaneous arrhythmia. AMELI
A has been experimented by using well-known datasets, namely Physionet-AF and NSR-DB. The achieved results provide evidence of the potential benefits of the approach, especially regarding sensitivity. AMELIA has an incredibly high potential to be used in applications of continuous monitoring, where the detection of AF episodes is a fundamental and crucial activity.
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