Comparison of wavelet‐based decomposition and empirical mode decomposition of electrohysterogram signals for preterm birth classification

Abstract

Signal decomposition is a computational technique that dissects a signal into its constituent components, providing supplementary information. In this study, the capability of two common signal decomposition techniques, including wavelet-based and empirical mode decomposition, on preterm birth classification was investigated. Ten time-domain features were extracted from the constituent components of electrohysterogram (EHG) signals, including EHG subbands and EHG intrinsic mode functions, and employed for preterm birth classification. Preterm birth classification and anticipation are crucial tasks that can help reduce preterm birth complications. The computational resultsshow that the preterm birth classification obtained using wavelet-based decomposition is superior. This, therefore, implies that EHG subbands decomposed through wavelet-based decomposition provide more applicable information for preterm birth classification. Furthermore, an accuracy of 0.9776 and a specificity of 0.9978, the best performance on preterm birth classification among state-of-the-art signal processing techniques, were obtained using the time-domain features of EHG subbands.

An open dataset with electrohysterogram records of pregnancies ending in induced and cesarean section delivery

“…In 2021, an important study 26 revealed that over-sampling applied after data partitioning, i.e., partition-synthesis over-sampling approach, needs to be applied to achieve realistic classification performance, and realistic preterm birth prediction in the case of imbalanced sets. Recently, many interesting studies related to preterm birth prediction using the TPEHG DB were published using traditional feature engineering 27 – 33 and deep learning 34 – 37 approaches. A nice review of the literature dealing with the use of EHG records for the task of predicting premature birth and for understanding the underlying physiological processes during pregnancy can be found in 38 .…”