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Evaluation of predictive capabilities of quantitative cardiotocographic signal features

100%

Czabański R. , Jeżewski M. , Roj D. , Szaszkowski Z. , Kupka T. , Wróbel J.

2010

tom Vol. 16

11--17

Cardiotocography (CTG) is the main method of assessment of the fetal state during pregnancy and labour used in clinical practice. It is based on quantitative analysis of fetal heart rate, fetal movements and uterine contractions signals. The evaluation of the CTG signals can be made using criteria recommended by International Federation of Obstetrics and Gynecology. Nevertheless, the diagnosis verification is possible only after the delivery on the basis of newborn assessment. In the proposed work we evaluated the capacity of quantitative analysis of CTG traces in predicting fetal outcome. The relationship between CTG signal features and attributes of fetal outcome was assessed on the basis of ROC curves analysis. The obtained results indicate the adequate predictive capabilities of the selected CTG features especially for fetal outcome assessed with Apgar score and suggest the necessity of applying the criteria for the CTG traces evaluation that are related to the gestational age. Our study also shows the value of the CTG monitoring as a screening procedure providing appropriate confirmation of fetal wellbeing.

Classification in the Gabor time-frequency domain of non-stationary signals embedded in heavy noise with unknown statistical distribution

84%

Świercz E.

International Journal of Applied Mathematics and Computer Science

2010

tom Vol. 20, no 1

135-147

A new supervised classification algorithm of a heavily distorted pattern (shape) obtained from noisy observations of nonstationary signals is proposed in the paper. Based on the Gabor transform of 1-D non-stationary signals, 2-D shapes of signals are formulated and the classification formula is developed using the pattern matching idea, which is the simplest case of a pattern recognition task. In the pattern matching problem, where a set of known patterns creates predefined classes, classification relies on assigning the examined pattern to one of the classes. Classical formulation of a Bayes decision rule requires a priori knowledge about statistical features characterising each class, which are rarely known in practice. In the proposed algorithm, the necessity of the statistical approach is avoided, especially since the probability distribution of noise is unknown. In the algorithm, the concept of discriminant functions, represented by Frobenius inner products, is used. The classification rule relies on the choice of the class corresponding to the max discriminant function. Computer simulation results are given to demonstrate the effectiveness of the new classification algorithm. It is shown that the proposed approach is able to correctly classify signals which are embedded in noise with a very low SNR ratio. One of the goals here is to develop a pattern recognition algorithm as the best possible way to automatically make decisions. All simulations have been performed in Matlab. The proposed algorithm can be applied to non-stationary frequency modulated signal classification and non-stationary signal recognition.

Applying algorithms for inverse solutions in classifying EEG signals

84%

Jagodzińska U.

Elektronika : konstrukcje, technologie, zastosowania

2013

tom Vol. 54, nr 9

144-147

The article presents selected algorithms in inverse solutions in the EEG signal. When undertaking the calculations, it was assumed that the data obtained from electrodes on the surface of the head were preprocessed. As a result of using these algorithms it is possible to specify both the areas of the brain that the signals come from and the current density of the signals read by means of electrodes placed on the surface of the head. On the basis of knowing the solution to the inverse problem, an attempt was made to select the features of the signals. Then t-statistics was used to differentiate and order them.

W artykule przedstawiono wybrane algorytmy rozwiązywania zagadnień odwrotnych w sygnale EEG. Przystępując do obliczeń założono, że dane uzyskane z elektrod rozmieszczonych na powierzchni głowy zostały wstępnie przetworzone. Wynikiem działania tych algorytmów jest lokalizacja obszarów mózgu, z których pochodzą sygnały oraz natężenia tych sygnałów odczytywanych za pomocą elektrod rozmieszczonych na powierzchni głowy. Znając rozwiązania zagadnienia odwrotnego podjęto też próbę selekcji cech. Wykorzystano t-statystykę do ich zróżnicowania i uszeregowania.

Fetal state assessment using fuzzy analysis of fetal heart rate signals - Agreement with the neonatal outcome

84%

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

Biocybernetics and Biomedical Engineering

2013

tom Vol. 33, no. 3

145--155

Fetal monitoring is based on analysis of fetal heart rate signal. Visual interpretation is difficult so computer-aided systems for quantitative analysis are commonly used. The clinical interpretation guidelines provided by FIGO (Fédération Internationale de Gynécologie et d'Obstétrique) were used to develop the weighted fuzzy scoring system for qualitative assessment of the fetal state. In this work, agreement of the fuzzy classification system with the neonatal outcome assessment was analyzed. Various datasets were evaluated, depending on interpretation method of the signals which were recorded from patients. The obtained results confirmed possibility of the efficient fetal state assessment using the fuzzy inference method proposed.