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Authors: Igor Souza and Daniel Dantas

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

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

Abstract: Sudden cardiac death and arrhythmia account for a large percentage of all deaths worldwide. 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 diagnose early 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. Traditionally, ECG signals are classified manually, requiring experience and great skill, while being time-consuming and prone to error. Thus, machine learning algorithms have been widely adopted because of their ability to perform complex data analysis. The objective of this study is to develop a classifier capable of classifying a patient’s ECG signals for the detection of arrhythmia in cli nical patients. We developed a convolutional neural network (CNN) with long short memory (LSTM) to identify five classes 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 98.12% and an F1-score of 99.72% in the classification of ventricular ectopic beats (V), and an accuracy of 97.39% and an F1-score of 95.25% in the classification of supraventricular ectopic beats (S). (More)

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Paper citation in several formats:
Souza, I. and Dantas, D. (2024). Cardiac Arrhythmia Detection in Electrocardiogram Signals with CNN-LSTM. In Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-684-2; ISSN 2184-4313, SciTePress, pages 304-310. DOI: 10.5220/0012362600003654

@conference{icpram24,
author={Igor Souza. and Daniel Dantas.},
title={Cardiac Arrhythmia Detection in Electrocardiogram Signals with CNN-LSTM},
booktitle={Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2024},
pages={304-310},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012362600003654},
isbn={978-989-758-684-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 13th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Cardiac Arrhythmia Detection in Electrocardiogram Signals with CNN-LSTM
SN - 978-989-758-684-2
IS - 2184-4313
AU - Souza, I.
AU - Dantas, D.
PY - 2024
SP - 304
EP - 310
DO - 10.5220/0012362600003654
PB - SciTePress

- Science and Technology Publications, Lda.
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