Data_Sheet_1_A Strategy for Classification of “Vaginal vs. Cesarean Section” Delivery: Bivariate Empirical Mode Decomposition of Cardiotocographic Recordings.PDF

We propose objective and robust measures for the purpose of classification of “vaginal vs. cesarean section” delivery by investigating temporal dynamics and complex interactions between fetal heart rate (FHR) and maternal uterine contraction (UC) recordings from cardiotocographic (CTG) traces. Multivariate extension of empirical mode decomposition (EMD) yields intrinsic scales embedded in UC-FHR recordings while also retaining inter-channel (UC-FHR) coupling at multiple scales. The mode alignment property of EMD results in the matched signal decomposition, in terms of frequency content, which paves the way for the selection of robust and objective time-frequency features for the problem at hand. Specifically, instantaneous amplitude and instantaneous frequency of multivariate intrinsic mode functions are utilized to construct a class of features which capture nonlinear and nonstationary interactions from UC-FHR recordings. The proposed features are fed to a variety of modern machine learning classifiers (decision tree, support vector machine, AdaBoost) to delineate vaginal and cesarean dynamics. We evaluate the performance of different classifiers on a real world dataset by investigating the following classifying measures: sensitivity, specificity, area under the ROC curve (AUC) and mean squared error (MSE). It is observed that under the application of all proposed 40 features AdaBoost classifier provides the best accuracy of 91.8% sensitivity, 95.5% specificity, 98% AUC, and 5% MSE. To conclude, the utilization of all proposed time-frequency features as input to machine learning classifiers can benefit clinical obstetric practitioners through a robust and automatic approach for the classification of fetus dynamics.

本研究旨在通过探究胎儿心率（FHR）与宫缩（UC）记录之间的时序动态和复杂交互，提出对“阴道分娩与剖宫产”的分类进行客观且稳健的度量。经验模态分解（EMD）的多变量扩展在保留多尺度间通道（UC-FHR）耦合的同时，提取了UC-FHR记录中的内在尺度。EMD的模态对齐特性导致了匹配信号的分解，在频域内容上，为选择针对当前问题的稳健且客观的时间-频率特征铺平了道路。具体而言，通过利用多变量内在模态函数的瞬时幅度和瞬时频率，构建了一类特征，以捕捉UC-FHR记录中的非线性和非平稳交互。所提出的特征被输入到多种现代机器学习分类器（决策树、支持向量机、AdaBoost）中，以描绘阴道和剖宫产动态。通过对真实世界数据集上不同分类器的性能进行评估，包括敏感性、特异性、ROC曲线下面积（AUC）和均方误差（MSE），发现所有提出的40个特征在AdaBoost分类器上提供了最佳精度，达到了91.8%的敏感性、95.5%的特异性、98%的AUC和5%的MSE。综上所述，将所有提出的时间-频率特征作为机器学习分类器的输入，可以通过一种稳健且自动的方法，为临床产科从业者提供对胎儿动态进行分类的便利。