Sparse learning for Intrapartum fetal heart rate analysis

Fetal Heart Rate (FHR) monitoring is used during delivery for fetal well-being assessment. Classically based on the visual evaluation of FIGO criteria, FHR characterization remains a challenging task that continuously receives intensive research efforts. Intrapartum FHR analysis is further complicated by the two different stages of labor (dilation and active pushing). Research works aimed at devising automated acidosis prediction procedures are either based on designing new advanced signal processing analyses or on efficiently combining a large number of features proposed in the literature. Such multi-feature procedures either rely on a prior feature selection step or end up with decision rules involving long lists of features. This many-feature outcome rule does not permit to easily interpret the decision and is hence not well suited for clinical practice. Machine-learning-based decision-rule assessment is often impaired by the use of different, proprietary and small databases, preventing meaningful comparisons of results reported in the literature. Here, sparse learning is promoted as a way to perform jointly feature selection and acidosis prediction, hence producing an optimal decision rule based on as few features as possible. Making use of a set of 20 features (gathering 'FIGO-like' features, classical spectral features and recently proposed scale-free features), applied to two large-size (respectively; 1800 and; 500 subjects), well-documented databases, collected independently in French and Czech hospitals, the benefits of sparse learning are quantified in terms of: (i) accounting for class imbalance (few acidotic subjects), (ii) producing simple and interpretable decision rules, (iii) evidences for differences between the temporal dynamics of active pushing and dilation stages, and (iv) of validity/generalizability of decision rules learned on one database and applied to the other one.