Feature Selection Scheme Based on Multi-time- scales for Analyzing Congestive Heart Failure
Feature Selection Scheme Based on Multi-time- scales for Analyzing Congestive Heart Failure
复制标题
基于多时间尺度的充血性心力衰竭特征选择方案
DOI：
10.21203/rs.3.rs-338866/v1
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发表时间：
2021
期刊：
影响因子：
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通讯作者：
刘澄玉
中科院分区：
文献类型：
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作者：
王春元;张亚涛;江兴娥;刘飞飞;张志民;魏守水;刘澄玉
提出了一种结合多时间尺度分析和心率变异性(HRV)分析的特征选择方法，用于充血性心力衰竭(CHF)的中早期诊断。在以往的CHF诊断研究中，研究人员倾向于通过寻找新的HRV特征来增加HRV特征的多样性，或者使用不同的机器学习算法来优化CHF和正常窦性心律对象(NSR)的分类。事实上，充分利用传统的HRV特征也可以提高分类精度。该方法根据传统的HRV特征构造多时间尺度特征矩阵，该特征在多时间尺度上表现出良好的稳定性，在不同时间尺度上表现出差异性。当将多尺度特征输入支持向量机分类器时，多尺度特征比传统的单时间尺度特征具有更好的性能，支持向量机分类器的结果显示出99.52%、100.00%和99.83%的灵敏度、特异度和准确率。这些结果表明，所提出的特征选择方法用于心力衰竭的自动诊断时，可以有效地减少冗余特征和计算量。
This paper proposed a feature selection method combined with multi-time-scales analysis and heart rate variability (HRV) analysis for middle and early diagnosis of congestive heart failure (CHF). In previous studies regarding the diagnosis of CHF, researchers have tended to increase the variety of HRV features by searching for new ones or to use different machine learning algorithms to optimize the classification of CHF and normal sinus rhythms subject (NSR). In fact, the full utilization of traditional HRV features can also improve classification accuracy. The proposed method constructs a multi-time-scales feature matrix according to traditional HRV features that exhibit good stability in multiple time-scales and differences in different time-scales. The multi-scales features yield better performance than the traditional single-time-scales features when the features are fed into a support vector machine (SVM) classifier, and the results of the SVM classifier exhibit a sensitivity, a specificity, and an accuracy of 99.52%, 100.00%, and 99.83%, respectively. These results indicate that the proposed feature selection method can effectively reduce redundant features and computational load when used for automatic diagnosis of CHF.