Open AccessComparison of wavelet‐based decomposition and empirical mode decomposition of electrohysterogram signals for preterm birth classification
Author(s) -
Janjarasjitt Suparerk
Publication year - 2022
Publication title -
etri journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.295
H-Index - 46
eISSN - 2233-7326
pISSN - 1225-6463
DOI - 10.4218/etrij.2021-0220
Subject(s) - hilbert–huang transform , anticipation (artificial intelligence) , pattern recognition (psychology) , computer science , signal (programming language) , time domain , artificial intelligence , wavelet , decomposition , speech recognition , chemistry , telecommunications , organic chemistry , white noise , computer vision , programming language
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 results show 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.