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首页> 外文期刊>Multimedia Tools and Applications >Analyzing electrocardiogram signals obtained from a nymi band to detect atrial fibrillation
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Analyzing electrocardiogram signals obtained from a nymi band to detect atrial fibrillation

机译：分析从NYMI带中获得的心电图信号检测心房颤动

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摘要

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.
机译：在本文中，我们提出了一种用于从可穿戴装置获得的心电图（ECG）信号中检测心房颤动（AF）的方法。该方法采用三种分类方法：神经网络（NNS），K-最近邻居（KNN）和决策树（DT）。使用投票系统组合来自三个分类器中的每一个的结果，以使最终决定为AF是否存在。要开发分类系统，我们使用缠绕的ECG监控设备的NYMI频段收集来自61个科目的数据。从这些信号中，我们提取了RR间隔，QS间隔和R峰值幅度的连续差异（RMSD）和Shannon熵（Shannon熵（SH）的根均方形。然后将这些性质用作培训分类器的特征。该分类器的准确性，敏感度，特异性和精度分别为97.94％，100.00％，96.72％和94.74％，分别用于具有六个特征的数据集。评估NNS，KNN和DT的集合方法。根据集合的规则，这些分类器之间的准确性，灵敏度，特异性和精度不同。对于AF的一致测定规则，假阳性降低，增加假阴性。对于NSR的一致测定规则，假阳性增加，假阴性减少。尽管每个分类器的准确性取决于具有集合方法的特征集，但可以保留AF检测的精度。

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  • 来源
    《Multimedia Tools and Applications》 |2020年第24期|15985-15999|共15页
  • 作者

    Keonsoo Lee; Sora Kim; Hyung Oh Choi; Jinseok Lee; Yunyoung Nam;

  • 作者单位

    Convergence Institute of Medical Information Communication Technology and Management Soonchunhyang University Asan Republic of Korea;

    Department of ICT Convergence Rehabilitation Engineering Soonchunhyang University Asan Republic of Korea;

    Department of Cardiology College of Medicine Soonchunhyang University Bucheon Republic of Korea;

    Department of Biomedical Engineering Wonkwang University School of Medicine Iksan Republic of Korea;

    Department of Computer Science and Engineering Soonchunhyang University Asan Republic of Korea;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Arrhythmia; Atrial fibrillation; Smartphone; Electrocardiogram;

    机译：心律失常;心房颤动;手机;心电图;

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