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Comput Biol Med. 2015 May;60:132-42. doi: 10.1016/j.compbiomed.2015.03.005. Epub 2015 Mar 14.
4
Multiscale wavelet p-leader based heart rate variability analysis for survival probability assessment in CHF patients.基于多尺度小波p-先导的心力衰竭患者生存概率评估的心率变异性分析
Annu Int Conf IEEE Eng Med Biol Soc. 2014;2014:2809-12. doi: 10.1109/EMBC.2014.6944207.
5
Detection of structural changes in tachogram series for the diagnosis of atrial fibrillation events.心动描记序列结构变化检测在心房颤动事件诊断中的应用。
Comput Math Methods Med. 2013;2013:373401. doi: 10.1155/2013/373401. Epub 2013 Apr 18.
6
High accuracy in automatic detection of atrial fibrillation for Holter monitoring.用于动态心电图监测的心房颤动自动检测的高精度。
J Zhejiang Univ Sci B. 2012 Sep;13(9):751-6. doi: 10.1631/jzus.B1200107.
7
Atrial fibrillation detection using an iPhone 4S.使用 iPhone 4S 检测心房颤动。
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A new landscape for stroke prevention in atrial fibrillation: focus on new anticoagulants, antiarrhythmic drugs, and devices.心房颤动卒中预防的新局面：关注新型抗凝药物、抗心律失常药物和器械。
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9
A simple method to detect atrial fibrillation using RR intervals.一种使用 RR 间期检测心房颤动的简单方法。
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10
Wavelet leader based multifractal analysis of heart rate variability during myocardial ischaemia.
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心房颤动中心率变异性的小波主导多重分形分析

Wavelet leader multifractal analysis of heart rate variability in atrial fibrillation.

作者信息

Gadhoumi Kais, Do Duc, Badilini Fabio, Pelter Michele M, Hu Xiao

机构信息

Department of Physiological Nursing, University of California, San Francisco, CA, USA.

David Geffen School of Medicine, University of California, Los Angeles, CA, USA.

出版信息

J Electrocardiol. 2018 Nov-Dec;51(6S):S83-S87. doi: 10.1016/j.jelectrocard.2018.08.030. Epub 2018 Aug 23.

DOI:10.1016/j.jelectrocard.2018.08.030
PMID:30177367
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC6263832/
Abstract

BACKGROUND

Accurate and timely detection of atrial fibrillation (AF) episodes is important in primarily and secondary prevention of ischemic stroke and heart-related problems. In this work, heart rate regularity of ECG inter-beat intervals was investigated in episodes of AF and other rhythms using a wavelet leader based multifractal analysis. Our aim was to improve the detectability of AF episodes.

METHODS

Inter-beat intervals from 25 ECG recordings available in the MIT-BIH atrial fibrillation database were analysed. Four types of annotated rhythms (atrial fibrillation, atrial flutter, AV junctional rhythm, and other rhythms) were available. A wavelet leader based multifractal analysis was applied to 5 min non-overlapping windows of each recording to estimate the multifractal spectrum in each window. The width of the multifractal spectrum was analysed for its discrimination power between rhythm episodes.

RESULTS

In 10 of 25 recordings, the width of multifractal spectrum was significantly lower in episodes of AF than in other rhythms indicating increased regularity during AF. High classification accuracy (95%) of AF episodes was achieved using a combination of features derived from the multifractal analysis and statistical central moment features.

CONCLUSIONS

An increase in the regularity of inter-beat intervals was observed during AF episodes by means of multifractal analysis. Multifractal features may be used to improve AF detection accuracy.

摘要

背景

准确及时地检测房颤（AF）发作对于缺血性中风和心脏相关问题的一级和二级预防至关重要。在这项研究中，使用基于小波首波的多重分形分析，研究了房颤发作及其他心律时心电图心跳间期的心率规律性。我们的目的是提高房颤发作的可检测性。

方法

分析了麻省理工学院-贝斯以色列女执事医疗中心房颤数据库中25份心电图记录的心跳间期。有四种类型的注释心律（房颤、房扑、房室交界性心律和其他心律）。对每份记录的5分钟非重叠窗口应用基于小波首波的多重分形分析，以估计每个窗口中的多重分形谱。分析多重分形谱的宽度在不同心律发作之间的辨别能力。

结果

在25份记录中的10份中，房颤发作时多重分形谱的宽度显著低于其他心律，表明房颤期间规律性增加。使用多重分形分析得出的特征与统计中心矩特征相结合，实现了房颤发作的高分类准确率（95%）。

结论

通过多重分形分析观察到房颤发作期间心跳间期的规律性增加。多重分形特征可用于提高房颤检测的准确性。

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