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Authors: Igor Souza and Daniel Dantas

Affiliation: Departamento de Computação, Universidade Federal de Sergipe, São Cristóvão, SE, Brazil

Keyword(s): Electrocardiography, ECG, Atrial Fibrillation.

Abstract: Electrocardiography is a frequently used examination technique for heart disease diagnosis. Electrocardiography is essential in the clinical evaluation of patients who have heart disease. Through the electrocardiogram (ECG), medical doctors can identify whether the cardiac muscle dysfunctions presented by the patient have an inflammatory origin and early diagnosis of serious diseases that primarily affect the blood vessels and the brain. The basis of arrhythmia diagnosis is the identification of normal and abnormal heartbeats and their classification into different diagnoses based on ECG morphology. Heartbeats can be divided into five categories: non-ectopic, supraventricular ectopic, ventricular ectopic, fusion, and unknown beats. It is difficult to distinguish these heartbeats apart on the ECG as these signals are typically corrupted by outside noise. The objective of this study is to develop a classifier capable of classifying a patient’s ECG signals for the detection of arrhythmi a in clinical patients. We developed a convolutional neural network (CNN) to identify five categories of heartbeats in ECG signals. Our experiment was conducted with ECG signals obtained from a publicly available MIT-BIH database. The number of instances was even out to five classes of heartbeats. The proposed model achieved an accuracy of 99.33% and an F1-score of 99.44% in the classification of ventricular ectopic beats (VEB). (More)

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Paper citation in several formats:
Souza, I. and Dantas, D. (2023). Cardiac Arrhythmia Classification in Electrocardiogram Signals with Convolutional Neural Networks. In Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-626-2; ISSN 2184-4313, SciTePress, pages 356-362. DOI: 10.5220/0011682800003411

@conference{icpram23,
author={Igor Souza and Daniel Dantas},
title={Cardiac Arrhythmia Classification in Electrocardiogram Signals with Convolutional Neural Networks},
booktitle={Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2023},
pages={356-362},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011682800003411},
isbn={978-989-758-626-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 12th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Cardiac Arrhythmia Classification in Electrocardiogram Signals with Convolutional Neural Networks
SN - 978-989-758-626-2
IS - 2184-4313
AU - Souza, I.
AU - Dantas, D.
PY - 2023
SP - 356
EP - 362
DO - 10.5220/0011682800003411
PB - SciTePress

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