多变量联合顺序复发网络用于从健康和病态心跳动态来表征多导线心电图时间序列
Xiaodong Yang1, Meihui Chen1, Yanlin Ren1
1School of Computer Science and Technology, China University of Mining and Technology, Xuzhou 221116, China.
Chaos (Woodbury, N.Y.)
|October 13, 2023
概括
这项研究引入了一种新方法,即多变量关节顺序复发网络 (MJORNs),用于分析心脏信号. 通过检查心电图 (ECG) 的复杂性,MJORNs可以有效地区分健康和患病的心脏动态.
科学领域:
- 心脏病学和非线性动力学
- 生物医学信号处理
- 网络科学应用 网络科学应用
背景情况:
- 对心脏系统的非线性动力学分析对临床研究至关重要.
- 复发图是分析时间序列数据的既定工具,包括心电图 (ECG).
- 现有的多变量心电图分析方法在捕捉复杂的心脏动态方面存在局限性.
研究的目的:
- 提出并验证一种新的方法,即多变量联合顺序复发网络 (MJORNs),用于评估多头电脑心电图时间序列.
- 评估健康和患有心脏病的心电图时间序列之间的非线性复杂性和相似性.
- 为了证明MJORN在区分心脏健康状况方面优于现有的基于网络的方法.
主要方法:
- 构建MJORNs通过表示12导电图时间序列作为网络节点.
- 利用从联合顺序模式重复图中对角线长度的作为网络边缘权重.
- 分析网络拓指标 (例如,平均加权路径长度,平均加权聚类系数) 以量化心脏系统的复杂性.
主要成果:
- MJORNs揭示了健康心脏系统和患病心脏系统之间明显的拓差异.
- 与健康个体相比,患有心脏病的个体在MJORNs中表现出减少的平均加权路径长度和增加的平均加权聚类系数.
- 疾病状态中ECG线索之间的相似性降低与非线性复杂性降低相关,这体现在MJORN拓学中.
结论:
- MJORNs提供了心脏系统中非线性复杂性的敏感度量,反映了从健康状态到疾病状态的变化.
- 与其他多变量复发网络方法相比,提出的MJORN方法在区分健康和病态心跳动态方面表现出卓越的表现.
- 在MJORNs的拓变化提供了关于受损的碎形结构和心脏系统疾病的改变动态的见解.
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