| Recording of maternal uterine pressure (UP) and fetal heart rate (FHR) during labour and delivery is a procedure referred to as cardiotocography. Delay or failure to recognize abnormal patterns in these recordings can result in a failure to prevent fetal injury. Clinical interpretation has been predominantly visual, creating significant problems of intra- and inter-subject variability, as well as significant debate about its utility due to the low specificity of visual interpretation that contributes to unnecessary interventions (Cesarean sections). Taking a more automated and objective approach, we modelled the UP-FHR signal pair, for the first time, as an input-output system using a system identification approach to estimate their dynamic relation in terms of an impulse response function. We also modelled FHR baseline with a linear fit and FHR variability unrelated to UP using the power spectral density computed from an auto-regressive model. Using a perinatal database of normal and pathological cases, we trained support-vector-machine classifiers with feature sets from these models. We used the classification in a detection process. We obtained the best results with a detector that combined the decisions of classifiers using both feature sets. It detected half of the pathological cases, with very few false positives (7.5%), one hour and forty minutes before delivery. This would leave sufficient time for an appropriate clinical response. These results clearly demonstrate the utility of our method for the early detection of cases needing clinical intervention. |
