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Authors: Cesar N. Silva ; Fernanda Lopes ; Jefferson A. Matos and Maria Claudia F. Castro

Affiliation: Electrical Engineering Department, Centro Universitario FEI, São Bernardo do Campo, Brazil

Keyword(s): Electrocardiogram (ECG), Cardiac Arrhythmia, Classification, Machine-Learning.

Abstract: Vital sign monitoring is becoming a part of our daily lives, emerging as a trend of smart wearable devices used to manage health. Cardiac arrhythmia is any variation in the normal heartbeat rhythm, causing the heart to beat improperly. This work presents a study on the classification of cardiac arrhythmias in 4 classes, Normal (N), Supraventricular Ectopic (SVE), Ventricular Ectopic (LV), and Fusion of Normal and Ventricular (F). Using the MIT-BIH Arrhythmia Database and the Classification Learner App from MATLAB® for training, it was possible to investigate 24 models, where the Subspace KNN Ensemble obtained the best accuracy (74.4%) and was later used for implementation in the suggested user interface application.

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Paper citation in several formats:
N. Silva, C.; Lopes, F.; A. Matos, J. and Claudia F. Castro, M. (2023). Arrythmia Classification Using MATLAB® Classification Learner App. In Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS; ISBN 978-989-758-631-6; ISSN 2184-4305, SciTePress, pages 220-225. DOI: 10.5220/0011666300003414

@conference{biosignals23,
author={Cesar {N. Silva}. and Fernanda Lopes. and Jefferson {A. Matos}. and Maria {Claudia F. Castro}.},
title={Arrythmia Classification Using MATLAB® Classification Learner App},
booktitle={Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS},
year={2023},
pages={220-225},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011666300003414},
isbn={978-989-758-631-6},
issn={2184-4305},
}

TY - CONF

JO - Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2023) - BIOSIGNALS
TI - Arrythmia Classification Using MATLAB® Classification Learner App
SN - 978-989-758-631-6
IS - 2184-4305
AU - N. Silva, C.
AU - Lopes, F.
AU - A. Matos, J.
AU - Claudia F. Castro, M.
PY - 2023
SP - 220
EP - 225
DO - 10.5220/0011666300003414
PB - SciTePress

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