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Automated ECG Signals Analysis for Cardiac Abnormality Detection and Classification

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Abstract

The electrocardiogram (ECG) is a critical, non-invasive tool for diagnosing cardiovascular diseases, offering insights into heart function. However, analyzing extended ECG data can be complex, requiring advanced computerized systems for effective diagnosis and classification. These systems must detect arrhythmias, manage data noise, and adapt to individual waveform variations, while ensuring model robustness across different populations and settings. The main goal of this study is to develop an ECG signal processing system that can accurately detect and classify various cardiac conditions. We propose a novel hybrid approach, classifying ECG signals into categories such as normal, left bundle branch block (LBBB), paced beat, right bundle branch block (RBBB), and supraventricular contraction (SVC) using a PhysioNet database. By applying discrete wavelet transform (DWT) and principal component analysis (PCA), we extracted six relevant features from each ECG category. These features were analyzed using an adaptive neuro-fuzzy inference system (ANFIS) classifier, achieving an overall classification accuracy of 99.44%, with average sensitivity and specificity of 99.36% and 99.84%, respectively. This system shows significant promise in enhancing the accuracy and efficiency of diagnosing cardiovascular diseases through ECG analysis.

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Data Availability

In this study, we used the publicly available MIT-BIH Arrhythmia Database from PhysioNet. Link to the original dataset: https://www.physionet.org/content/mitdb/1.0.0/.

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Funding

This work is supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (RS-2023-00240521) and project for Industry-University-Research Institute platform cooperation R&D funded by Korea Ministry of SMEs and Startups in 2022 (S3310765).

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Authors and Affiliations

  1. School of Biomedical Engineering, Jimma University, 378, Jimma, Ethiopia

    Ahmed Mohammed Abagaro, Gelan Ayana, Ahmed Ali Dawud & Bheema Lingaiah Thamineni

  2. Department of Artificial Intelligence Convergence, Pukyong National University, 48513, Busan, Korea

    Hika Barki

  3. Department of Medical IT Convergence Engineering, Kumoh National Institute of Technology, 39253, Gumi, Korea

    Gelan Ayana & Se-woon Choe

  4. Faculty of Electrical and Computer Engineering, Jimma Institute of Technology, Jimma University, 378, Jimma, Ethiopia

    Towfik Jemal

  5. Department of IT Convergence Engineering, Kumoh National Institute of Technology, 39253, Gumi, Korea

    Se-woon Choe

Authors
  1. Ahmed Mohammed Abagaro
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Contributions

All authors contributed to the research design of the study. A.M.A., and S.-w.C.: conceptualization, data creation, analysis, and interpretation of data, analyzed the results, and drafted the manuscript. G.A., A.A.D., data analysis, resource investigation, validation, proofreading and editing of the manuscript. H.B., formal analysis, critical revision of the manuscript. B.L.T., T.J and S.-w.C.: Conceptualization, supervision, visualization, writing review, and editing. All authors approved the final version of the manuscript and agreed to be accountable for all aspects of the work. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Ahmed Mohammed Abagaro or Se-woon Choe.

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Conflict of interest

The authors declare no conflicts of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

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Abagaro, A.M., Barki, H., Ayana, G. et al. Automated ECG Signals Analysis for Cardiac Abnormality Detection and Classification. J. Electr. Eng. Technol. (2024). https://doi.org/10.1007/s42835-024-01902-y

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