[1]刘松,刘东晖,蒙庆华,等.阵发性房颤信号的稳定性分析与识别[J].中国医学物理学杂志,2025,42(9):1221-1228.[doi:DOI:10.3969/j.issn.1005-202X.2025.09.014]
　LIU Song,LIU Donghui,et al.Stability analysis and recognition of paroxysmal atrial fibrillation signals[J].Chinese Journal of Medical Physics,2025,42(9):1221-1228.[doi:DOI:10.3969/j.issn.1005-202X.2025.09.014]
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阵发性房颤信号的稳定性分析与识别()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第9期
页码:
1221-1228
栏目:
医学信号处理与医学仪器
出版日期:
2025-09-30

文章信息/Info

Title:
Stability analysis and recognition of paroxysmal atrial fibrillation signals
文章编号:
1005-202X（2025）09-1221-08
作者:
刘松1; 2; 刘东晖1; 2; 蒙庆华2; 贺德华2
1.南宁师范大学广西信息功能材料与智能信息处理重点实验室， 广西 南宁 530001； 2.南宁师范大学物理与电子学院， 广西 南宁 530001
Author(s):
LIU Song1;  2;  LIU Donghui1;  2;  MENG Qinghua2;  HE Dehua2
1. Guangxi Key Laboratory of Information Functional Materials and Intelligent Information Processing, Nanning Normal University, Nanning 530001, China 2. School of Physics and Electronics, Nanning Normal University, Nanning 530001, China
关键词:
阵发性房颤检测; 稳定性分析; 动态模态; 特征提取
Keywords:
Keywords: paroxysmal atrial fibrillation detection stability analysis dynamic mode feature extraction
分类号:
R318；TN957.52
DOI:
DOI:10.3969/j.issn.1005-202X.2025.09.014
文献标志码:
A
摘要:
心房颤动（房颤）的阵发性发作因其短暂性和随机性导致临床识别困难，现有动态模态分解方法在处理单通道含噪心电信号时存在模态和特征提取不足的问题。该研究提出基于高阶动态模态分解的信号分析方法，通过张量分解技术捕捉心电信号的高阶相关性，将复杂信号分解为具有明确物理意义的动态模态。基于模态交互关系构建信号子系统稳定性评价体系，结合反映系统不稳定性的模态数量占比、模态分布混乱度及特征值谱差异等4类量化指标，建立阵发性房颤的特征判别模型。实验采用MIT-BIH心房颤动数据库（AFDB）验证，结果显示房颤组在系统稳定性相关特征上呈现显著差异（P<0.05）。基于支持向量机的分类模型实现96.15%的平均识别准确率。这表明该方法通过解析心电信号非线性动力学特征，为房颤的早期预警和精准诊断提供新的量化分析框架。
Abstract:
Abstract: The clinical detection of paroxysmal atrial fibrillation (PAF) remains challenging due to its transient and stochastic characteristics, and existing dynamic mode decomposition methods have limitations in modal redundancy reduction and feature extraction when processing single-channel noisy electrocardiogram (ECG) signals. Therefore, a signal analysis method based on high-order dynamic mode decomposition is proposed. It captures high-order correlations within ECG signals through tensor decomposition techniques and decomposes complex signals into physically interpretable dynamic modes. A stability evaluation framework for signal subsystem is established based on modal interaction relationships. By incorporating quantitative indicators including proportion of modes reflecting system instability, modal distribution entropy, and eigenvalue spectrum divergence, a feature discrimination model for PAF is developed. Experimental validation using the MIT-BIH atrial fibrillation database reveals statistically significant differences (P<0.05) in stability-related features between PAF episodes and normal sinus rhythms. The classification model based on support vector machine achieves an average recognition accuracy of 96.15%. These results demonstrate that the proposed method can effectively analyze nonlinear dynamic characteristics in noisy single-lead ECG signals, thereby establishing a novel quantitative analytical framework for early detection and accurate diagnosis of PAF.

备注/Memo

备注/Memo:
【收稿日期】2025-04-03 【基金项目】广西高校中青年教师科研基础能力提升项目（2023KY0398）；广西”青苗人才”科研项目 【作者简介】刘松，博士，助理研究员，研究方向：新能源电力系统分析与控制、数字信号处理，E-mail: songliu86@126.com 【通信作者】贺德华，博士，讲师，研究方向：自动化控制、故障诊断，E-mail: 153367542@qq.com
更新日期/Last Update: 2025-09-30