[1]牛晓东,柴国强,王大为,等.基于心率变异性的阵发性心房颤动预测方法[J].中国医学物理学杂志,2024,41(5):579-587.[doi:DOI:10.3969/j.issn.1005-202X.2024.05.008]
　NIU Xiaodong,CHAI Guoqiang,et al.Prediction of paroxysmal atrial fibrillation based on heart rate variability analysis[J].Chinese Journal of Medical Physics,2024,41(5):579-587.[doi:DOI:10.3969/j.issn.1005-202X.2024.05.008]
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基于心率变异性的阵发性心房颤动预测方法()
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《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
41卷
期数:
2024年第5期
页码:
579-587
栏目:
医学信号处理与医学仪器
出版日期:
2024-05-23

文章信息/Info

Title:
Prediction of paroxysmal atrial fibrillation based on heart rate variability analysis
文章编号:
1005-202X（2024）05-0579-09
作者:
牛晓东1; 2; 柴国强3; 王大为3; 卢莉蓉1; 韩玲娜4; 连亚军5
1.长治医学院生物医学工程系， 山西 长治 046000； 2.长治医学院山西省智能数据辅助诊疗工程研究中心， 山西 长治 046000； 3.山西师范大学物理与信息工程学院， 山西 太原 030000； 4.长治医学院生理学教研室， 山西 长治 046000； 5.长治医学院附属和平医院全科医疗科， 山西 长治 046000
Author(s):
NIU Xiaodong1;  2;  CHAI Guoqiang3;  WANG Dawei3;  LU Lirong1;  HAN Lingna4;  LIAN Yajun5
1. Department of Biomedical Engineering, Changzhi Medical College, Changzhi 046000, China 2. Shanxi Engineering Research Center for Intelligent Data Assisted Diagnosis and Treatment, Changzhi Medical College, Changzhi 046000, China 3. School of Physics and Information Engineering, Shanxi Normal University, Taiyuan 030000, China 4. Department of Physiology, Changzhi Medical College, Changzhi 046000, China 5. Department of General Practice, Heping Hospital Affiliated to Changzhi Medical College, Changzhi 046000, China
关键词:
阵发性房颤; 心率变异性; 熵; 尺度; 积分均值模式分解
Keywords:
Keywords: paroxysmal atrial fibrillation heart rate variability entropy scale integral mean mode decomposition
分类号:
R318.04
DOI:
DOI:10.3969/j.issn.1005-202X.2024.05.008
文献标志码:
A
摘要:
基于心率变异性（HRV）的特征分析，提出一种患者阵发性房颤（PAF）发作的预测系统方法。首先，基于一种新的自适应滤波技术逐次平滑滤波并粗粒化HRV后，采用熵量化HRV在多个自适应尺度的复杂性特征；其次，特征经Min-Max归一化和序列前向选择特征子集，输入支持向量机识别HRV类型，预测PAF发作。经50例时长5 min HRV序列集的五折交叉验证，得到最优预测结果为：准确率98%，敏感性100%，特异性96%，性能表现优越。另外，实验表明远离和紧随PAF时的HRV复杂性特征值在不同频率段内，分别具有不同的显著变化（P<0.05），反映受试者神经系统调节心脏节律改变，以及调控机体、应激等适应外界环境变化能力的下降。
Abstract:
Abstract: Based on the analysis of heart rate variability (HRV), a prediction method for paroxysmal atrial fibrillation (PAF) attacks is proposed. A new adaptive filtering technique is used for smoothing and coarse graining of HRV, followed by entropy-based quantification of HRV complexity at multiple adaptive scales. After the features are normalized by Min-Max, feature subsets are selected by sequential forward selection method, and then input to support vector machine to identify HRV types and predict PAF attacks. Through 5-fold cross-validation on a set of 50 HRV sequences each lasting 5 minutes, the optimal prediction results are obtained: 98% accuracy, 100% sensitivity, 96% specificity, demonstrating excellent performance. In addition, the experiment shows significant changes (P<0.05) in the complexity eigenvalues of HRV far away from and close to PAF at different frequency bands, reflecting alterations in nervous system regulation of cardiac rhythm and a decline in the ability to adapt to external environmental changes such as stress regulation.

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备注/Memo

备注/Memo:
【收稿日期】2023-12-13 【基金项目】国家自然科学基金（62201332，62201333）；山西省基础研究计划（自由探索类）（20210302124328）；长治医学院博士科研启动基金（BS202123） 【作者简介】牛晓东，博士，副教授，研究方向：生物医学信号处理，E-mail: nuixd@czmc.edu.cn
更新日期/Last Update: 2024-05-24