Patricio Fuentealba, Alfredo Illanes, Frank Ortmeier: A Study on the Classification Performance of Cardiotocographic Data vs Class Formation Criteria. In: Forthcoming.

Abstract

Fetal monitoring during labor is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction data obtained by a Cardiotocograph (CTG). Currently, the interpretation of such data is difficult because it involves a visual analysis of highly complex signals. For this reason, several approaches based on signal processing and classification have been proposed. Most of the CTG classification approaches use class formation criteria based on the pH value, which is considered as a gold standard measure for postpartum evaluation. However, at birth, the association of a precise value of pH with the neonatal outcome is still inconclusive, which makes the classification training a difficult task. This work focuses on studying the CTG classification performance in relation to the used class formation criterion. For this purpose, first, the FHR signal is decomposed by using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, we extract a set of signal features based on CEEMDAN and conventional time-domain features proposed in the literature, which are computed in different FHR signal lengths just before delivery. Then, the features classification performance is evaluated according to a set of class formation criteria based on different pH values used as thresholds. Results reveal that the classification performance significantly depends on the selected pH value for the class formation, whose best performance is achieved by considering a class formation based on a pH=7.05.

    BibTeX (Download)

    @inproceedings{fuentealba2019study,
    title = {A Study on the Classification Performance of Cardiotocographic Data vs Class Formation Criteria},
    author = {Patricio Fuentealba and Alfredo Illanes and Frank Ortmeier},
    year  = {2019},
    date = {2019-11-24},
    abstract = {Fetal monitoring during labor is commonly based on the joint recording of the fetal heart rate (FHR) and uterine contraction data obtained by a Cardiotocograph (CTG). Currently, the interpretation of such data is difficult because it involves a visual analysis of highly complex signals. For this reason, several approaches based on signal processing and classification have been proposed. Most of the CTG classification approaches use class formation criteria based on the pH value, which is considered as a gold standard measure for postpartum evaluation. However, at birth, the association of a precise value of pH with the neonatal outcome is still inconclusive, which makes the classification training a difficult task. This work focuses on studying the CTG classification performance in relation to the used class formation criterion. For this purpose, first, the FHR signal is decomposed by using the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) method. Second, we extract a set of signal features based on CEEMDAN and conventional time-domain features proposed in the literature, which are computed in different FHR signal lengths just before delivery. Then, the features classification performance is evaluated according to a set of class formation criteria based on different pH values used as thresholds. Results reveal that the classification performance significantly depends on the selected pH value for the class formation, whose best performance is achieved by considering a class formation based on a pH=7.05.},
    keywords = {biomedical signal processing, cardiotocograph, empirical mode decomposition, fetal heart rate},
    pubstate = {forthcoming},
    tppubtype = {inproceedings}
    }