Examination of Single Wavelet-Based Features of EHG Signals for Preterm Birth Classification

摘要

In this study, wavelet-based features of electrohys-terogram (EHG) quantifying the electrical activity of uterine muscles are applied for preterm birth classification. EHG has been shown to provide useful information for uterine contraction that leads to the anticipation of delivery. A wavelet-based feature referred to as △_l in this study is determined from a difference between the logarithms of variances of detail coefficients of EHG data corresponding to two consecutive levels, i.e., l and l + 1. Performance on preterm birth classifications using single wavelet-based features of EHG data is examined. A simple thresholding technique is applied for preterm birth classifications. The leave-one-out cross validation is used to validate the performance on preterm birth classifications. From the computational results, it is shown that the wavelet-based features of EHG can provide a reasonable performance on preterm birth classification with the accuracy, the sensitivity and the specificity of 0.7099, 0.6842 and 0.7133, respectively.