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Objective: Monitoring of the fetal condition during labor is currently performed by cardiotocograpy (CTG). Despite the use of CTG in clinical practice, CTG interpretation suffers from a high inter- and intra-observer variability and a low specificity. In addition to CTG, analysis of fetal heart rate variability (HRV) has been shown to provide information on fetal distress. However, fetal HRV can be strongly influenced by uterine contractions, particularly during the second stage of labor. Therefore, the aim of this study is to examine if distinguishing contractions from rest periods can improve the detection rate of HRV features for fetal distress during the second stage of labor. Approach: We used a dataset of 100 recordings, containing 20 cases of fetuses with adverse outcome. The most informative HRV features were selected by a genetic algorithm and classification performance was evaluated using support vector machines. Main results: Classification performance of fetal heart rate segments closest to birth improved from a geometric mean of 70% to 79%. If the classifier was used to indicate fetal distress over time, the geometric mean at 15 minutes before birth improved from 60% to 72%. Significance: Our results show that combining contraction-dependent HRV features with HRV features calculated over the entire fetal heart rate signal improves the detection rate of fetal distress.