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Early predicting a risk of preterm labour by analysis of antepartum electrohysterograhic signals

Horoba K., Jezewski J., Matonia A., Wrobel J., Czabanski R., Jezewski M.

Biocybernetics and Biomedical Engineering

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2016

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Vol. 36, no. 4

574--583

EN

This study is aimed at evaluation of the capability to indicate the preterm labour risk by analysing the features extracted from the signals of electrical uterine activity. Free access database was used with 300 signals acquired in two groups of pregnant women who delivered at term (262 cases) and preterm (38 cases). Signal features comprised classical time domain description, spectral parameters and nonlinear measures of contractile activity. Their mean values were calculated for all the contraction episodes detected in each record and their statistical significance for recognition of two groups of recordings was provided. Obtained results were related to the previous study where the same features were applied but they were determined for entire signals. Influence of electrodes location, band-pass filter settings and gestation week was investigated. The obtained results showed that a spectral parameter – the median frequency was the most promising indicator of the preterm labour risk.

2

Improving the efficacy of automated fetal state assessment with fuzzy analysis of delivery outcome

Czabanski R., Jezewski M., Horoba K., Jezewski J., Leski J.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

223--230

EN

A number of methods of the qualitative assessment of fetal heart rate (FHR) signals are based on supervised learning. The classification methods based on the supervised learning require a set of training recordings accompanied by the reference interpretation. In the real data collections the class of signals related to fetal distress is usually under-represented. Too small percentage of distress patterns adversely affects the effectiveness of the automated evaluation of the fetal state. The paper presents a method of equalizing the class sizes based on the reference assessment of the fetal state with the fuzzy analysis of the newborn attributes. The supervised learning with increased number of the FHR signals, which are characterized by the highest rate of the fuzzy inference leads to significant increase of the efficacy of the qualitative assessment of the fetal state using the Lagrangian support vector machine.

3

Efficiency of automated detection of uterine contraction using tocography

Horoba K., Wrobel J., Jezewski J., Kupka T., Czabanski R., Roj D., Jezewski M.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

207--214

EN

Monitoring of uterine contractile activity enables to control the progress of labour. Automated detection of contractions is to be an integral part of the signal analysis implemented in computer aided fetal surveillance system. Evaluation of efficiency of three algorithms for automated detection of uterine contractions in the signal of uterine mechanical activity is presented. These algorithms are based generally on analysis of the frequency distribution of signal values. The reference data in form of beginning and end of contraction episodes were obtained from human expert. Obtained results showed high efficiency of the algorithms tested where the best one ensured the sensitivity and positive predictive value equal to 92.2 and 97.2, respectively.

4

Analysis of electrical uterine contractile activity for prediction of preterm delivery

Horoba K., Jezewski J., Matonia A., Wrobel J., Czabanski R., Jezewski M.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

199--205

EN

This study is aimed at evaluation of the capability to indicate the preterm delivery risk analysing the features extracted from signals of electrical uterine activity. Free access database was used with signals acquired in two groups of pregnant women who delivered at term and preterm. Signal features comprised classical time domain and spectral parameters of contractile activity, as well as the sample entropy. Their mean values were calculated over all contraction episodes detected in each record and their statistical significance for separating the two groups of recordings was provided. Influence of electrodes location, band-pass filter settings and gestation week was investigated. The obtained results showed that a spectral parameter – the median frequency was the most promising indicator of the preterm delivery risk.

5

A new approach for the clustering using pairs of prototypes

Jezewski M., Czabanski R., Leski J., Horoba K.

Journal of Medical Informatics & Technologies

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2015

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Vol. 24

113--121

EN

In the presented work two variants of the fuzzy clustering approach dedicated for determining the antecedents of the rules of the fuzzy rule-based classifier were presented. The main idea consists in adding additional prototypes (’prototypes in between’) to the ones previously obtained using the fuzzy c-means method (ordinary prototypes). The ’prototypes in between’ are determined using pairs of the ordinary prototypes, and the algorithm based on distances and densities finding such pairs was proposed. The classification accuracy obtained applying the presented clustering approaches was verified using six benchmark datasets and compared with two reference methods.

6

Reconstruction of FHR series recorded via ultrasound - method validation using abdominal fetal electrocardiography

Kupka T., Horoba K., Roj D., Matonia A., Czabanski R., Jezewski M.

Journal of Medical Informatics & Technologies

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2013

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Vol. 22

135--141

EN

Analysis of variability of the fetal heart rate (FHR) is very important for fetal wellbeing assessment. The beat-to-beat variability is described quantitatively by the indices originated from invasive fetal electrocardiography which provides the FHR signal in a form of time event series. Nowadays, monitoring instrumentation is based on Doppler ultrasound technology. The fetal monitors provide the output signal in a form of evenly spaced measurements. The goal of this work is to present a new method for the FHR signal processing, which enables extraction of time series of consecutive heartbeat intervals from the evenly repeated values. The proposed correction algorithm enables recognition and removal of the duplicated measurements. Reliable evaluation of the algorithm requires the reference event series, thus the FHR signals were obtained from abdominal fetal electrocardiograms to be used in this research study.