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Analyzing electrocardiogram signals obtained from a nymi band to detect atrial fibrillation

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Abstract

In this paper, we propose a method for detecting atrial fibrillation (AF) from electrocardiogram (ECG) signals obtained from a wearable device. The proposed method uses three classification methods: neural networks (NNs), k-nearest neighbors (kNN), and decision trees (DT). The results from each of the three classifiers are combined using a voting system to make the final decision as to whether AF is present. To develop the classification system, we collected data from 61 subjects using a Nymi Band that is wrist-worn ECG monitoring device. From these signals, we extracted the root-mean square of the successive differences (RMSSD) and the Shannon entropy (ShE) of the RR interval, QS interval, and R peak amplitude. These properties were then used as features to train the classifiers. The accuracy, sensitivity, specificity, and precision of this classifier were 97.94%, 100.00%, 96.72%, and 94.74%, respectively for dataset with six features. The ensemble method of NNs, kNN, and DT was evaluated. Depending on the rules for ensemble, the accuracy, sensitivity, specificity, and precision are different among those classifiers. With a rule of unanimous determination for AF, false positive is decreased and false negative is increased. With a rule of unanimous determination for NSR, false positive is increased and false negative decreased. Even though accuracies of each classifier are depending on the set of features, with ensemble method, the accuracy of AF detection can be preserved.

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Data Availability

We made the test sets available at https://sites.google.com/site/yynams/afib_nymi.

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Acknowledgements

This work was supported by the Soonchunhyang University Research Fund and also supported by the Bio & Medical Technology Development Program of the NRF funded by the Korean Government, MSIP(NRF-2015M3A9D7067219).

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Authors and Affiliations

  1. Convergence Institute of Medical Information Communication Technology and Management, Soonchunhyang University, Asan, Republic of Korea

    Keonsoo Lee

  2. Department of ICT Convergence Rehabilitation Engineering, Soonchunhyang University, Asan, Republic of Korea

    Sora Kim

  3. Department of Cardiology, College of Medicine, Soonchunhyang University, Bucheon, Republic of Korea

    Hyung Oh Choi

  4. Department of Biomedical Engineering, Wonkwang University School of Medicine, Iksan, Republic of Korea

    Jinseok Lee

  5. Department of Computer Science and Engineering, Soonchunhyang University, Asan, Republic of Korea

    Yunyoung Nam

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  1. Keonsoo Lee
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  2. Sora Kim
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  3. Hyung Oh Choi
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  4. Jinseok Lee
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  5. Yunyoung Nam
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Correspondence to Yunyoung Nam.

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The authors declare that they have no conflict of interest.

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All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. The study was approved by the Ethics Committee of the Bucheon Hospital of Soonchunhyang University (SCHBC 2017–01–006-002).

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Lee, K., Kim, S., Choi, H.O. et al. Analyzing electrocardiogram signals obtained from a nymi band to detect atrial fibrillation. Multimed Tools Appl 79, 15985–15999 (2020). https://doi.org/10.1007/s11042-018-7075-1

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  • DOI: https://doi.org/10.1007/s11042-018-7075-1

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