School of Electrical and Automatic Engineering, Changshu Institute of Technology, Suzhou 215500, People's Republic of China.
Physiol Meas. 2020 Aug 11;41(7):074002. doi: 10.1088/1361-6579/ab998d.
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).
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.
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%.
The proposed method has the potential to detect EBr subjects by the ABP signal.
本研究旨在探讨动脉血压（ABP）信号在检测危及生命的极重度心动过缓（EBr）患者中的潜力。
所提出方法的步骤包括 ABP 信号预处理、ABP 波分段、模型参数估计和 EBr 患者检测。首先，在预处理中消除噪声、干扰和异常段。然后，通过心搏周期将 ABP 信号分段为一系列 ABP 波。提出脉搏分解分析（PDA）方法来定量描述 ABP 波的变化。采用反向传播神经网络、概率神经网络和决策树（DT）设计分类器，通过 PDA 模型的参数来区分 EBr 患者和健康受试者。利用 Fantasia 国际健康受试者生理信号数据库和 2015 年 PhysioNet/CinC 挑战赛 EBr 患者数据库验证所提出的方法，并提取了 79310 个健康受试者和 4595 个 EBr 患者的 ABP 波。
我们获得了健康受试者和 EBr 患者的平均 PDA 模型，并得出了它们的变化。双样本 Kolmogorov-Smirnov 检验结果表明，健康受试者和 EBr 患者的所有模型参数均存在显著差异（H=1，P<0.05）。分类结果表明，决策树（DT）具有最佳性能，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%。
该方法具有通过 ABP 信号检测 EBr 患者的潜力。
