This paper presents a new hybrid fuzzy clustering method. In the proposed method, cluster prototypes are values that minimize the introduced generalized cost function. The proposed method can be considered as a generalization of fuzzy c–means (FCM) method as well as the fuzzy c–median (FCMed) clustering method. The generalization of the cluster cost function is made by applying the Lp norm. The values that minimize the proposed cost function have been chosen as the group prototypes. The weighted myriad is the special case of the group prototype, when the Lp norm is the L2 (Euclidean) norm. The cluster prototypes are the weighted meridians for the L1 norm. Artificial data set is used to demonstrate the performance of proposed method.
In this paper we discussed the influence of preliminary processing of the ultrasound Doppler signal on accuracy of the fetal heart rate estimation as well as on reliability of the FHR instantaneous variability assessment. We attempted to develop an optimal processing channel of US Doppler signal in order to measure the periodicity of fetal heart activity with accuracy as close as possible to that ensured by FECG. The FHR values determined from the US signal were compared to the reference data obtained from direct FECG. In a final evaluation we used the parameters describing the FHR variability as the clinically important signal features being the most sensitive to any periodicity inaccuracy. The results proved that an application of proposed algorithms improves the accuracy of interval measurements and FHR instantaneous variability assessment in relation to the new-generation fetal monitors.
The most important features indicating appropriate fetal development are the measures of instantaneous variability of a fetal heart rate (FHR), describing fluctuations of the beat-to-beat heart intervals. The most popular method for the FHR acquisition is the Doppler ultrasound technique. However, it is very sensitive to various motion artifacts distorting the signal being acquired. The aim of our work was to evaluate the influence of signal loss episodes on the parameters quantitatively describing the instantaneous variability of the FHR. For this purpose we artificially inserted signal loss episodes to the recordings, in different patterns and percentage, in accordance with the real characteristics of the signal loss segments. We particularly would like to answer the question if the signals with significant amount of signal loss can be reliably evaluated by means of instantaneous variability measures, and which of these measures (numerical indices) are more robust to the missing values.
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.
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.
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