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1

The influence of cardiotocogram signal feature selection method on fetal state assessment efficacy

Jeżewski M., Czabański R., Łęski J.

Journal of Medical Informatics & Technologies

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2014

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Vol. 23

51--58

EN

Cardiotocographic (CTG) monitoring is a method of assessing fetal state. Since visual analysis of CTG signal is difficult, methods of automated qualitative fetal state evaluation on the basis of the quantitative description of the signal are applied. The appropriate selection of learning data influences the quality of the fetal state assessment with computational intelligence methods. In the presented work we examined three different feature selection procedures based on: principal components analysis, receiver operating characteristics and guidelines of International Federation of Gynecology and Obstetrics. To investigate their influence on the fetal state assessment quality the benchmark SisPorto® dataset and the Lagrangian support vector machine were used.

2

Analysis of extracted cardiotocographic signal features to improve automated prediction of fetal outcome

Jeżewski M., Czabański R., Wróbel J., Horoba K.

Biocybernetics and Biomedical Engineering

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2010

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Vol. 30, no. 4

29-47

EN

Cardiotocographic monitoring based on automated analysis of the fetal heart rate (FHR) signal is widely used for fetal assessment. However, the conclusion generation system is still needed to improve the abnormal fetal outcome prediction. Classification of the signals according to the predicted fetal outcome by means of neural networks is presented in this paper. Multi-layer perceptron neural networks were learned through seventeen time-domain signal features extracted during computerized analysis of 749 traces from 103 patients. The analysis included estimation of the FHR baseline, detection of acceleration and deceleration patterns as well as measurement of the instantaneous FHR variability. All the traces were retrospectively verified by the real fetal outcome defined by newborn delivery data. Influence of numerical and categorical representation of the input signal features, different data sets during learning, and gestational age as additional information, were investigated. We achieved the best sensitivity and specificity for the neural networks fed with numerical input variables together with additional information on the gestational age in the categorical form.

3

Unsupervised clustering for fetal state assessment based on selected features of the cardiotocographic signals

Przybyła T., Jeżewski J., Roj D.

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

157--162

EN

In modern obstetrics the cardiotocography is a routine method of fetal condition assessment based mainly on analysis of the fetal heart rate signals. The correct interpretation of recorded traces from a bedside monitor is very difficult even for experienced clinicians. Therefore, computerized fetal monitoring systems are used to yield the quantitative description of the signal. However, the effective techniques enabling automated conclusion generation based on cardiotocograms are still being searched. The paper presents an attempt to diagnose the fetal state basing on seventeen features describing the cardiotocographic records. The proposed method applies the unsupervised classification of signals. During our research we tried to classify the fetal state using the fuzzy c-means (FCM) clustering. We also tested how the efficiency of classification could be influenced by application of principal component analysis (PCA) algorithm. The obtained results showed that unsupervised classification cannot be considered as a support to fetal state assessment.

4

Influence of gestational age on neural networks interpretation of fetal monitoring signals

Jeżewski M., Czabański R., Horoba K., Wróbel J., Łęski J., Jeżewski J.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

137--142

EN

Cardiotocographic monitoring (CTG) is a primary biophysical monitoring method for assessment of the fetal state and is based on analysis of fetal heart rate, uterine contraction activity and fetal movement signals. Visual analysis of CTG traces is very difficult so computer-aided fetal monitoring systems have become a standard in clinical centres. We proposed the application of neural networks for the prediction of fetal outcome using the parameters of quantitative description of acquired signals as inputs. We focused on the influence of the gestational age (during trace recording) on the fetal outcome classification quality. We designed MLP and RBF neural networks with changing the number of neurons in the hidden layer to find the best structure. Networks were trained and tested fifty times, with random cases assignment to training, validating and testing subset. We obtained the value of sensitivity index above 0.7, what may be regarded as good result. However additional trace grouping within similar gestational age, increased classification quality in the case of MLP networks.

5

Recognition of fetus defects on the base of computer analysis of fetal heart rate signal

Kurzyński M., Morawiec B., Tomaszek M.

Journal of Medical Informatics & Technologies

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2000

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Vol. 1

BI 51--57

EN

The present paper deals with the experimental comparative analysis of method and algorithms of fetal heart rate (FHR) signal processing. The purpose of FHR signal processing is computer-aided recognition of uncomplicated pregnancy, pregnancy with arterial hypertension and with immunological conflict. In the discussed methods we can distinguish two steps: reduction of information and decision making. In the first step two different methods of time and frequency analysis of FHR signal were applied: Schur algorithm of linear optimal mean square prediction and morphological features describing acceleration and deceleration phenomena in the FHR signal. In the second step however, we used three-layer artificial neural network of back propagation type with sigmoid transfer functions and pattern recognition approach with minimum-distance algorithms. In the former method the following procedures were applied: nearest mean algorithm, nearest neighbour and 3-nearest neighbour algorithms. Experimental investigations using MATLAB 5.2 program were made and their results are presented in Table 1. These investigations which are sequel to the previous experimental studies proves that information contained in the FHR signal can be source of valuable and interesting knowledge on the fetus defects.

PL

Praca przedstawia wyniki eksperymentalnej analizy porównawczej wybranych metod i algorytmów przetwarzania sygnału częstości akcji serca płodu (FHR) w celu komputerowego wspomagania rozpoznawania ciąży fizjologicznej, ciąży powikłanej konfliktem serologicznym oraz ciąży powikłanej nadciśnieniem tętniczym. Przedyskutowane metody różnią się procedurami redukcji cech oraz algorytmami decyzyjnymi. Do wydobycia parametrów opisujących sygnał FHR wykorzystano metody przetwarzania w dziedzinie czasu i częstotliwości, a jako algorytmy klasyfikacji zastosowano minimalno-odległościowe algorytmy rozpoznawania oraz sieć neuronową propagacji wstecznej.