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Impact of ECG Signal Preprocessing and Filtering on Arrhythmia Classification Using Machine Learning Techniques

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Advances in Artificial Intelligence – IBERAMIA 2022 (IBERAMIA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13788))

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Abstract

Cardiac arrhythmias are heartbeat disorders in which the electrical impulses that coordinate the cardiac cycle malfunction. The heart’s electrical activity is recorded using electrocardiography (ECG), a non-invasive method that helps diagnose several cardiovascular diseases. However, interpretation of ECG signals can be difficult due to the presence of noise, the irregularity of the heartbeat, and their nonstationary nature. Hence, the use of computational systems is required to support the diagnosis of cardiac arrhythmias. The main challenge in developing AI-assisted ECG systems is achieving accuracies suitable for application in clinical settings. Therefore, this paper introduces a software tool for classifying cardiac arrhythmias in ECG recordings that uses filtering, segmentation, and feature extraction of the QRS interval. We use the MIT-BIH Arrhythmia Database, which has 48 records of five different types of arrhythmias. We evaluate the data using supervised machine learning techniques such as k-Nearest Neighbors (KNN), Random Forest (RF), Multilayer Perceptron (MLP), and the Naive Bayesian classifier. This paper shows the impact of selecting and employing filtering and feature extraction methods on the performance of supervised machine learning algorithms compared with benchmark approaches.

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Acknowledgments

The authors would like to acknowledge the valuable support given by the SDAS Research Group (https://sdas-group.com/).

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Authors and Affiliations

  1. Universidad Mariana, Grupo de investigación de Ingeniería Mecatrónica, Pasto, Colombia

    Hermes Andrés Ayala-Cucas, Edison Alexander Mora-Piscal & Dagoberto Mayorca-Torres

  2. Modeling, Simulation and Data Analysis (MSDA) Research Program, Mohammed VI Polytechnic University, Ben Guerir, Morocco

    Diego Hernán Peluffo-Ordoñez

  3. Departamento de Lenguajes y Sistemas Informáticos, Universidad de Granada, C/Periodista Daniel Saucedo Aranda s/n, 18071, Granada, Spain

    Dagoberto Mayorca-Torres & Alejandro J. León-Salas

Authors
  1. Hermes Andrés Ayala-Cucas
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  2. Edison Alexander Mora-Piscal
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  3. Dagoberto Mayorca-Torres
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  4. Diego Hernán Peluffo-Ordoñez
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  5. Alejandro J. León-Salas
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Corresponding author

Correspondence to Dagoberto Mayorca-Torres .

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Editors and Affiliations

  1. Federal University of Rio de Janeiro, Rio de Janeiro, Brazil

    Ana Cristina Bicharra Garcia

  2. Fluminense Federal University, Niterói, Brazil

    Mariza Ferro

  3. University of Cartagena, Cartagena de Indias, Colombia

    Julio Cesar Rodríguez Ribón

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Andrés Ayala-Cucas, H., Mora-Piscal, E.A., Mayorca-Torres, D., Peluffo-Ordoñez, D.H., León-Salas, A.J. (2022). Impact of ECG Signal Preprocessing and Filtering on Arrhythmia Classification Using Machine Learning Techniques. In: Bicharra Garcia, A.C., Ferro, M., Rodríguez Ribón, J.C. (eds) Advances in Artificial Intelligence – IBERAMIA 2022. IBERAMIA 2022. Lecture Notes in Computer Science(), vol 13788. Springer, Cham. https://doi.org/10.1007/978-3-031-22419-5_3

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  • DOI: https://doi.org/10.1007/978-3-031-22419-5_3

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