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Publicação
Unsupervised Classification of Uterine Contractions Recorded Using Electrohysterography
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Orientador(es)
Resumo(s)
Pregnancy still poses health risks that are not attended to by current clinical practice
motorization procedures. Electrohysterography (EHG) record signals are analyzed in the
course of this thesis as a contribution and effort to evaluate their suitability for pregnancy
monitoring.
The presented work is a contributes with an unsupervised classification solution for
uterine contractile segments to FCT’s Uterine Explorer (UEX) project, which explores
analysis procedures for EHG records.
In a first part, applied processing procedures are presented and a brief exploration of
the best practices for these. The procedures include those to elevate the representation of
uterine events relevant characteristics, ease further computation requirements, extraction
of contractile segments and spectral estimation.
More detail is put into the study of which characteristics should be chosen to represent
uterine events in the classification process and feature selection methods. To such end,
it is presented the application of a principal component analysis (PCA) to three sets:
interpolated contractile events, contractions power spectral densities, and to a number of
computed features that attempt evidencing time, spectral and non-linear characteristics
usually used in EHG related studies.
Subsequently, a wrapper model approach is presented as a mean to optimize the feature
set through cyclically attempting the removal and re-addition of features based on clustering
results. This approach takes advantage of the fact that one class is known beforehand to
use its classification accuracy as the criteria that defines whether the modification made
to the feature set was ominous.
Furthermore, this work also includes the implementation of a visualization tool that
allows inspecting the effect of each processing procedure, the uterine events detected by
different methods and clusters they were associated to by the final iteration of the wrapper
model.
Descrição
Palavras-chave
Electrohysterogram (EHG) Electromyogram (EMG) Unsupervised Classification Machine Learning Signal Processing