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Computational Vision and Bio-Inspired Computing pp 729–741Cite as

Classification of ECG Arrhythmias Using Conventional Tree-Based Machine Learning Approaches

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Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1439))

Abstract

The electrocardiogram (ECG) wave is a procedure that uses electrodes to monitor the electrical signals of the heart. This signal provides crucial details regarding many cardiac diseases that can affect the heart of an individual. Prevalent approaches for the analysis and classification of the ECG waveforms include conventional signal processing and machine learning (ML) techniques. This paper presents the classification of ECG waveforms employing tree-based ML approaches. Using the MIT-BIH gold standard dataset, these ML models were trained, and several performance indices were evaluated during the testing phase. Besides, both wrapper and filter-based feature selection (FS) techniques were employed to extract significant features. The outcomes of this study suggest that the LGB with wrapper-based FS outperformed others by ensuring peak values for accuracy (91.04%) of ECG classification as well as for other performance indices like specificity (97%), precision (91%), recall (91%), etc.

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Acknowledgements

This study was funded by the Khulna University Research and Innovation Centre, Khulna University, Bangladesh.

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Authors and Affiliations

  1. Electronics and Communication Engineering Discipline, Khulna University, Khulna, 9208, Bangladesh

    Abdullah Al-Mamun Bulbul, Md. Bellal Hossain, Mainul Islam Labib & Abdullah-Al Nahid

Authors
  1. Abdullah Al-Mamun Bulbul
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  2. Md. Bellal Hossain
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  3. Mainul Islam Labib
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  4. Abdullah-Al Nahid
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Corresponding author

Correspondence to Abdullah Al-Mamun Bulbul .

Editor information

Editors and Affiliations

  1. Department of Electronics and Communication Engineering, RVS Technical Campus, Coimbatore, India

    S. Smys

  2. Departamento de Engenharia Mecânica, Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  3. Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA

    Fuqian Shi

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Bulbul, A.AM., Hossain, M.B., Labib, M.I., Nahid, AA. (2023). Classification of ECG Arrhythmias Using Conventional Tree-Based Machine Learning Approaches. In: Smys, S., Tavares, J.M.R.S., Shi, F. (eds) Computational Vision and Bio-Inspired Computing. Advances in Intelligent Systems and Computing, vol 1439. Springer, Singapore. https://doi.org/10.1007/978-981-19-9819-5_52

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