Abstract
This work aims at presenting a method for heartbeat classification based on multi-layer perceptron (MLP) and random forest (RF) techniques applied to the first difference of ECG signals. From the MIT-BIH Arrhythmia Database, each annotated P-QRS segment was extracted, low-pass filtered, and the first-order difference was used as input for the neural networks. The MLP and Random Forest were used to obtain a model for classifying the heartbeats. The results were compared with other algorithms existing in the literature, and the model developed produced good results and noticed improvements when using the first difference.
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References
Guyton, A., Hall, J.: Textbook of Medical Physiology. Elsevier Saunders, Amsterdam (2015)
Luz, E., Schwartz, W., Cámara-Chavez., G., Menotti, D.: ECG-based heartbeat classification for arrhythmia detection: a survey. Comput. Methods Programs Biomed. 127, 144–164 (2016)
Luz, E., Nunes, T., Papa, J., Menotti, D.: ECG arrhythmia classification based on optimum-path forest. Expert Syst. Appl. 40, 3561–3573 (2013)
Ye, C., Kumar, B., Coimbra, M.: Combining general multi-class and specific two-class classifiers for improved customized ECG heartbeat classification. In: 21st International Conference on Pattern Recognition ICPR 2012, pp. 2428–2431, Tsukuba, Japan (2012)
Dalvi, R., Zago, G., Aandreão, R.: Heartbeat classification system based on neural networks and dimensionality reduction. Res. Biomed. Eng. 32(4), 318–326 (2016)
Koeppen, B., Stanton, B.: Berne & Levy Fisiologia. Elsevier, Rio de Janeiro (2009)
Pinheiro, P.: Eletrocardiograma (ECG): Entenda os resultados (2021). https://www.mdsaude.com/cardiologia/exame-eletrocardiograma-ecg/
ANSI/AAMI EC57:2012, testing and reporting performance results of cardiac rhythm and ST segment measurement algorithms. American National Standards Institute, Inc. (2012)
Moody, G., Mark, R.: The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 20, 45–50 (2001)
Prati, R.C., Batista, G.E.A.P.A., Monard, M.C.: A study with class imbalance and random sampling for a decision tree learning system. In: Bramer, M. (ed.) IFIP AI 2008. ITIFIP, vol. 276, pp. 131–140. Springer, Boston, MA (2008). https://doi.org/10.1007/978-0-387-09695-7_13
Rasesh, K., Dhuli, R.: Classification of imbalanced ECG beats using re-sampling techniques and adaboost ensemble classifier. Biomed. Sig. Process. Control 41(4), 242–254 (2018)
Sannino, G., Pietro, G.D.: A deep learning approach for ECG-based heartbeat classification for arrhythmia detection. Futur. Gener. Comput. Syst. 86, 446–455 (2018)
Acknowledgment
This study was supported by the Brazilian Agencies FINEP, CAPES, and CNPq.
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Ortiz, C.A.L., Nadal, J. (2024). Heartbeat Classification Using MLP and Random Forest Techniques. In: Marques, J.L.B., Rodrigues, C.R., Suzuki, D.O.H., Marino Neto, J., García Ojeda, R. (eds) IX Latin American Congress on Biomedical Engineering and XXVIII Brazilian Congress on Biomedical Engineering. CLAIB CBEB 2022 2022. IFMBE Proceedings, vol 99. Springer, Cham. https://doi.org/10.1007/978-3-031-49404-8_44
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DOI: https://doi.org/10.1007/978-3-031-49404-8_44
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