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Optimized multi-stage sifting approach for ECG arrhythmia classification with shallow machine learning models

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Abstract

Early diagnosis of illness is critical for timely initiation of treatment and ultimately curing of the patient. This is especially important for diseases in which fatality rate is high like heart ailments. The approach suggested in this work utilizes the efficacy of different feature sets and shallow machine learning approaches for detection of various classes of arrhythmia. In this work a multi-stage sifting approach for arrhythmia detection has been suggested. For classification of arrhythmia, eleven number of shallow machine learning models have been studied. The arrhythmia detection performance was compared using various metrics such as accuracy, precision, recall (sensitivity), specificity, and F1-Scores (F-measure). To further improve the classification models, optimized weighting was applied on top three performing classifier models. Among the four different optimizers evaluated, the Whale Optimization Algorithm (WOA) and Particle Swarm Optimizer (PSO) emerged as the top performers. The proposed method exhibited substantial improvements compared to existing models, with an average increase of 3.1% in overall accuracy. Additionally, all other parameters such as precision, recall, sensitivity, and F1-Scores showed an average improvement of around 8%.

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Data availability

The datasets utilized in the research are publicly available to everyone over the internet.

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Authors and Affiliations

  1. Electrical Engineering Department, NIT Hamirpur, Hamirpur, HP, 177005, India

    Piyush Mahajan & Amit Kaul

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  1. Piyush Mahajan
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  2. Amit Kaul
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Correspondence to Piyush Mahajan.

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Mahajan, P., Kaul, A. Optimized multi-stage sifting approach for ECG arrhythmia classification with shallow machine learning models. Int. j. inf. tecnol. 16, 53–68 (2024). https://doi.org/10.1007/s41870-023-01641-9

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