A recognition method for extreme bradycardia by arterial blood pressure signal modeling with curve fitting
A recognition method for extreme bradycardia by arterial blood pressure signal modeling with curve fitting
复制标题
一种基于曲线拟合的动脉血压信号建模的极度心动过缓识别方法
DOI：
10.1088/1361-6579/ab998d
复制
发表时间：
2020-07-01
影响因子：
3.2
通讯作者：
Gu, Ya
中科院分区：
文献类型：
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作者：
Chou, Yongxin;Zhang, Aihua;Gu, Ya
目的：本研究的目的是探讨潜在的动脉血压（ABP）信号检测的受试者与危及生命的极端心动过缓（EBr）。方法：该方法包括ABP信号预处理、ABP波分割、模型参数估计和EBr主体检测。首先，在预处理中消除噪声、干扰和异常段。然后，ABP信号被分割成一系列的ABP波心动周期。脉冲分解分析（PDA）的方法，提出了定量描述ABP波的变化。利用BP神经网络、概率神经网络和决策树（DT）设计分类器，通过PDA模型参数对EBr患者和健康人进行分类。利用国际生理信号数据库Fantasia和2015 PhysioNet/CinC Challenge对该方法进行了验证，提取了79 310个健康人ABP波形和4595个EBr人ABP波形。主要结果：我们获得了健康人和EBr受试者的平均PDA模型，并推导了它们的变化。两样本Kolmogorov-Smirnov检验结果显示，正常人与EBr组间各模型参数均存在显著性差异（H= 1，P < 0.05）。分类结果表明，DT的特异性为99.74% ± 0.07%，敏感性为93.12% ± 1.24%，准确性为99.37% ± 0.10%，kappa系数为93.92% ± 0.92%。意义：所提出的方法具有通过ABP信号检测EBr受试者的潜力。
Objective: The aim of this study is to investigate the potential of arterial blood pressure (ABP) signal for the detection of the subjects with life-threatening extreme bradycardia (EBr). Approach: The steps of the proposed method include ABP signal preprocessing, ABP wave segmentation, model parameter estimation, and EBr subject detection. First, the noise, interference and abnormal segments are eliminated in the pre-processing. Then, the ABP signal is segmented into a series of ABP waves by cardiac cycles. The pulse decomposition analysis (PDA) approach is presented to quantitively describe the changes in ABP waves. The back-propagation neural network, probabilistic neural network and decision tree (DT) are engaged to design the classifiers to discriminate the EBr subjects from healthy subjects by the parameters of PDA models. The international physiological signal databases of Fantasia for healthy subjects and 2015 PhysioNet/CinC Challenge for EBr subjects are exploited to validate the proposed method, and 79 310 ABP waves of healthy subjects and 4595 ABP waves of EBr subjects are extracted. Main results: We obtain the average PDA models of healthy subjects and EBr subjects and derive their changes. The two-sample Kolmogorov–Smirnov test result shows that all model parameters are markedly different (H= 1, P < 0.05) between the healthy and EBr subjects. The classification results show that the DT has the best performance with specificity of 99.74% ± 0.07%, sensitivity of 93.12% ± 1.24%, accuracy of 99.37% ± 0.10% and kappa coefficient of 93.92% ± 0.92%. Significance: The proposed method has the potential to detect EBr subjects by the ABP signal.