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Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network

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Abstract

Atrial fibrillation (AF) is a cardiac arrhythmia which is characterized based on the irregsular beating of atria, resulting in, the abnormal atrial patterns that are observed in the electrocardiogram (ECG) signal. The early detection of this pathology is very helpful for minimizing the chances of stroke, other heart-related disorders, and coronary artery diseases. This paper proposes a novel method for the detection of AF pathology based on the analysis of the ECG signal. The method adopts a multi-rate cosine filter bank architecture for the evaluation of coefficients from the ECG signal at different subbands, in turn, the Fractional norm (FN) feature is evaluated from the extracted coefficients at each subband. Then, the AF detection is carried out using a deep learning approach known as the Hierarchical Extreme Learning Machine (H-ELM) from the FN features. The proposed method is evaluated by considering normal and AF pathological ECG signals from public databases. The experimental results reveal that the proposed multi-rate cosine filter bank based on FN features is effective for the detection of AF pathology with an accuracy, sensitivity and specificity values of 99.40%, 98.77%, and 100%, respectively. The performance of the proposed diagnostic features of the ECG signal is compared with other existing features for the detection of AF. The low-frequency subband FN features found to be more significant with a difference of the mean values as 0.69 between normal and AF classes.

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Funding

This work has been funded by outstanding potential for excellence in research and academics (OPERA) award, BITS Pilani with grant number as FR/SCM/150618/EEE.

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Authors and Affiliations

  1. MLR Institute of Technology, Hyderabad, India

    S. K. Ghosh

  2. Birla Institute of Technology and Science Pilani, Hyderabad, India

    R. K. Tripathy

  3. National Autonomous University of Mexico (UNAM), Mexico City, Mex. 04510, Mexico

    Mario R. A. Paternina

  4. Sanatorio G̈uemes, Buenos Aires, 01188, Argentina

    Juan J. Arrieta

  5. Michoacan University of Saint Nicholas of Hidalgo (UMSNH), Morelia, Mich. 58030, Mexico

    Alejandro Zamora-Mendez

  6. Biomedical Engineering and Neuromorphic Systems (BENS) Research Group, MARCS Institute, Western Sydney University, Penrith, New South Wales, Australia

    Ganesh R. Naik

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  1. S. K. Ghosh
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Correspondence to R. K. Tripathy.

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Conflict of interests

RK Tripathy has received research grants from BITS Pilani, Hyderabad Campus. SK Ghosh declares that he has no conflict of interest. Mario A. Paternina declares that he has no conflict of interest. Juan J. Arrieta declares that he has no conflict of interest., Alexandro Zamora-Mendez declares that he has no conflict of interest. Ganesh R. Naik declares that he has no conflict of interest.

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This article does not contain any studies with human participants or animals performed by any of the authors. However, authors have used ECG signals from various public databases. We have cited the references for the public databases in the manuscript.

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Ghosh, S.K., Tripathy, R.K., Paternina, M.R.A. et al. Detection of Atrial Fibrillation from Single Lead ECG Signal Using Multirate Cosine Filter Bank and Deep Neural Network. J Med Syst 44, 114 (2020). https://doi.org/10.1007/s10916-020-01565-y

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