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Heptagonal Reinforcement Learning (HRL): a novel algorithm for early prevention of non-sinus cardiac arrhythmia

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Abstract

There have been many connections between medical science and artificial intelligence in recent years. Many problems arise with the integrity of communication. Cardiac arrhythmia, carried out using artificial intelligence methods, is one of the most dangerous diseases in the field of prevention. Topics introduced in artificial intelligence are the automatic selection of balancing and classification algorithms. In this study, metrics for machine learning algorithm selection are presented. The first problem is the problem of choosing the best balancing algorithm to balance the data sets, introduced as triangle rate (TR). The second issue to be studied is selecting the best automatic classification algorithm. The third action was to use a scoring algorithm to predict sinus and non-sinus arrhythmias. The heptagonal reinforcement learning (HRL) achieved results competitive with standard algorithms by combining three types of algorithms. The data used in this study was a 12-lead electrocardiogram (ECG) database of arrhythmias. The number of patients examined in this dataset is 10,646. The HRL algorithm has improved the previous algorithms by 5%, achieving 86% cardiac arrhythmia prediction.

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Availability of data and materials

The dataset used in this research is available in the link below and free access is available for all researchers. Data Info.

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Authors and Affiliations

  1. Department of Computer Engineering, Islamic Azad University, Karaj Branch, Karaj, Iran

    Arman Daliri, Roghaye Sadeghi, Neda Sedighian, Abbas Karimi & Javad Mohammadzadeh

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  1. Arman Daliri
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  2. Roghaye Sadeghi
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  3. Neda Sedighian
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  4. Abbas Karimi
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  5. Javad Mohammadzadeh
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Contributions

AD Participated in conceptualization, designing the algorithm, visualization, writing and reviewing the manuscript draft, Correction of the article in revisions by the journal and experimental implementations. RS Engaged in visualization and experimental implementations. NS Responsible for writing and reviewing the manuscript. AK Responsible for reviewing the manuscript draft, and supervising. JM Supervised the research project by assisting all co-authors in conceptualization and reviewing the manuscript.

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Correspondence to Javad Mohammadzadeh.

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Daliri, A., Sadeghi, R., Sedighian, N. et al. Heptagonal Reinforcement Learning (HRL): a novel algorithm for early prevention of non-sinus cardiac arrhythmia. J Ambient Intell Human Comput 15, 2601–2620 (2024). https://doi.org/10.1007/s12652-024-04776-0

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