Separating sets of term and pre-term uterine electromyogram records using sample entropy

Vlada Semenova (2016) Separating sets of term and pre-term uterine electromyogram records using sample entropy. MSc thesis.

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Abstract

Predicting preterm labour is a serious and unresolved problem. In this master thesis I focused on resolving the problem of separation and classifi- cation of groups of uterine electromyographic recordings with the term and pre-term birth, which were recorded before 26th week and after 26th week of gestation. Until now, studies have been made where they separated the two groups in the frequency range with a lower limit of 0.08 Hz to 0.3 Hz and highest limit of 3 Hz or 4 Hz. In this master thesis, I focused on the area between 0.08 Hz and 5 Hz, since these areas are not well researched. I used the sample entropy as one of the currently most promising techniques and international reference database TPEHG DB of uterine electromyographic recordings. For the classification of recordings I tested the K nearest neighbors (K-NN) classifier, linear and quadratic discriminant analysis (LDA, QDA) classifiers, Naive Bayes, support vector machine method and decision trees. To assess the degree of separation of groups of recordings I used a statistical analysis of variance (ANOVA). Because of the uneven distribution of the number of recordings that were preterm and term, I used Synthetic Minority Over-sampling Technique (SMOTE), in order to ensure more real results. The result of the master’s thesis is that it was confirmed that the classification between preterm and term births on the recordings that were recorded early the best area was from 1 Hz - 5 Hz. We have also found that by increasing of the frequency or by widening of the frequency area, preterm recordings become less regular and less predictable; term recordings on the contrary, become more regular and more predictable. The achieved results of classification of recordings with term and preterm delivery using frequency band from 1 Hz and 2.2 Hz, and sample entropy features only, (Sensitivity 88.8 %, Specificity 81.8 %, Classification accuracy 85.3 %) are quite comparable to achieved results of classification of other existing studies (Sensitivity 89 %, Specificity 91 %, Classification accuracy 90 %) which, besides sample entropy features, also involved other signal features, signal spectra features, as well as additional clinical information about pregnancies.

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