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Authors: Giovanni Rosa 1 ; Gennaro Laudato 1 ; Angela Colavita 2 ; Simone Scalabrino 1 and Rocco Oliveto 1

Affiliations: 1 STAKE Lab, University of Molise, Pesche (IS), Italy ; 2 ASREM, Regione Molise, Italy

ISBN: 978-989-758-490-9

ISSN: 2184-4305

Keyword(s): Arrhythmia, ECG, Machine Learning, Decision Support Systems.

Abstract: With the spread of Internet of Medical Things (IoMT) systems, the scientific community has dedicated a lot of effort in the definition of approaches for supporting specialized staff in the early diagnosis of pathological conditions and diseases. Several approaches have been defined for the identification of arrhythmia, a pathological condition that can be detected from an electrocardiogram (ECG) trace. There exist many types of arrhythmia and some of them present a great impact on the patients in terms of worsening of physical conditions or even mortality. In this work we present NEAPOLIS, a novel approach for the accurate detection of arrhythmia conditions. NEAPOLIS takes as input a heartbeat signal, extracted from an ECG trace, and provides as output a 5-class classification of the beat, namely normal sinus rhythm and four main types of arrhythmia conditions. NEAPOLIS is based on ECG characteristics that do not need a long-term observation of an ECG for the classification of the be at. This choice makes NEAPOLIS a (near) real-time detector of arrhythmia because it allows the detection within few seconds of ECG observation. The accuracy of NEAPOLIS has been compared to one of the best and most recent work from the literature. The achieved results show that NEAPOLIS provides a more accurate detection of arrhythmia conditions. (More)

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Paper citation in several formats:
Rosa, G.; Laudato, G.; Colavita, A.; Scalabrino, S. and Oliveto, R. (2021). Automatic Real-time Beat-to-beat Detection of Arrhythmia Conditions. In Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF, ISBN 978-989-758-490-9; ISSN 2184-4305, pages 212-222. DOI: 10.5220/0010267902120222

@conference{healthinf21,
author={Giovanni Rosa. and Gennaro Laudato. and Angela Colavita. and Simone Scalabrino. and Rocco Oliveto.},
title={Automatic Real-time Beat-to-beat Detection of Arrhythmia Conditions},
booktitle={Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,},
year={2021},
pages={212-222},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010267902120222},
isbn={978-989-758-490-9},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 14th International Joint Conference on Biomedical Engineering Systems and Technologies - HEALTHINF,
TI - Automatic Real-time Beat-to-beat Detection of Arrhythmia Conditions
SN - 978-989-758-490-9
IS - 2184-4305
AU - Rosa, G.
AU - Laudato, G.
AU - Colavita, A.
AU - Scalabrino, S.
AU - Oliveto, R.
PY - 2021
SP - 212
EP - 222
DO - 10.5220/0010267902120222

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