In this thesis we present automatic analysis of electromyogram of uterus (electrohysterogram) using coherence function which is one of non-linear signal processing techniques. We used records of international reference database TPEHG DB (Term-Preterm Electrohysterogram DataBase), which contains 300 electrohysterogram records. We preprocessed signals with nine different band-pass Butterworths filters with forward-backward filtering to avoid zero phase shift.
Separation of groups took place in two variants, among early recorded and among late recorded records. We calculated coherence function between all pairs of records for each of variants. For calculation we used power spectrum of signals. Coherence estimation for whole frequency range, was made with two techniques - median amplitude and integral.
Analysis of variance or ANOVA showed which frequency ranges and signals are useful for preterm - term records separation. For records classification we used frequency intervals and signals with p-value less than 0,05. Evaluation of classification was made on Bayes classifier, decision trees and our own built classifier. We developed it empirically, based on coherence decreasing among term records from frequency range 1-2,5 Hz to 2,5-3,5 Hz. Performance evaluation of classification is done in three ways - on training set, on the principle of training-testing set and with the approach "omitted one". Best results were shown with decision tree at frequency range 0,3-2,5 Hz, where sensitivity was 95 %, specificity and accuracy were 98 %. With our own developed classifier we reach sensitivity between 58 % and 63 % and specificity between 58 % and 63 %. Classification using Bayes classifier did not show good results, having sensitivity close to 0 %.
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