| Abstract: |  Cardiotocography (CTG) consists of fetal heart rate (FHR) and uterine pressure measurements. CTG has become a standard in obstetrics and is widely used in clinical settings nowadays. Fetal distress during labour causes firstly a loss of FHR variations, and secondly an occurrence of long lasting decelerations after uterine contractions. Automatic assessment of these changes is, however, not fully reliable. This thesis focused on assessment of fetal behaviour using nonlinear methods, such as fractal dimension, entropy and complexity measures. Performance of these methods was examined on short segments of fetal heart rate and evaluated in terms of ability to distinguish between normal and pathological fetuses. Using Mann-Whitney U test, it was found that all employed methods revealed a difference in feature's median values of significance p < 0.01. Performed feature selection rated following methods as the most suitable for further classification task: Lempel Ziv complexity, waveform fractal dimension, and sample entropy. The classification results proved that nonlinear methods provide useful information of fetus status even for fetal heart rate segment of short duration. The achieved classification performance, by support vector machine, was in terms of sensitivity and specificity 78% and 70%, respectively.
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