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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.

2

Fuzzy system for evaluation of fetal heart rate signals using FIGO criteria

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

Journal of Medical Informatics & Technologies

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2009

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Vol. 13

189--194

EN

Cardiotocography is a biophysical method of fetal monitoring during pregnancy and labour. It is mainly based on recording and analysis of fetal heart activity. The computerized fetal monitoring systems provide the quantitative description of the recorded signals but the effective methods supporting the conclusion generation are still needed. The evaluation of the signal can be made using criteria recommended by FIGO. Nevertheless, the quantitative description of the traces is inconsistent with qualitative nature of the obstetric knowledge. Therefore, we applied the fuzzy system based on Takagi-Sugeno-Kang model to evaluate and classify signals. FIGO guidelines were used for developing a set of fuzzy conditional rules defining the system performance. The proposed system was evaluated using data collected with computerized fetal surveillance system – MONAKO. The classification results confirm the improvement of the fetal state evaluation quality while using the proposed fuzzy system support.

3

The influence of window size of autocorrelation function on fetal heart rate variability measurement using the Doppler ultrasound signal

Roj D., Fuchs T., Przybyła T., Jeżewski M., Matonia A., Gacek A.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

111--116

EN

Commonly used noninvasive fetal monitoring is based on fetal heart rate (FHR) variability analysis of the Doppler ultrasound signal coming from the mechanical activity of the fetal heart. Estimation of periodicity of acquired signals using the autocorrelation technique is very important. The determination of cardiac intervals using the Doppler signal is more difficult than in electrocardiography, where the R-waves are evident. We investigated the influence of the autocorrelation window size on the FHR variability analysis. The indices describing the FHR variability calculated for signals obtained using two different autocorrelation techniques with various window lengths were compared with the reference ones obtained from fetal electrocardiogram. The optimal window was a compromise between artifacts resistance and the averaging level of instantaneous variability.

4

Separation of abdominal fetal electrocardiograms in twin pregnancy

Kotas M., Jeżewski J., Matonia A., Kupka T.

Journal of Medical Informatics & Technologies

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2008

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Vol. 12

83--89

EN

A combined application of independent component analysis and projective filtering of the time-aligned ECG beats is proposed to solve the problem of fetal ECG extraction from multi-channel maternal abdominal electric signals. The developed method is employed to process the four-channel abdominal signals recorded during twin pregnancy. The signals are complicated mixtures of the maternal ECG, the ECGs of the fetal twins and noise of other origin. The independent component analysis cannot separate the respective signals, but the proposed combination of the methods allows to suppress the maternal ECG and when the level of noise is low it leads to an effective separation of the twins' signals.

5

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.