Improving cardiotocography monitoring: a memory-less stream learning approach Position Paper

Cardiotocography is widely used, all over the world, for fetal heart rate and uterine contractions monitoring before (antepartum) and during (intrapartum) labor, regarding the detection of fetuses in danger of death or permanent damage. However, analysis of cardiotocogram tracings remains a large and unsolved issue. State-of-the-art monitoring systems provide quantitative parameters that are difficult to assess by the human eye. These systems also trigger alerts for changes in the behavior of the signals. However, they usually take up to 10 min to detect these different behaviors. Previous work using machine learning for concept drift detection has successfully achieved faster results in the detection of such events. Our aim is to extend the monitoring system with memory-less fading statistics, which have been successfully applied in drift detection and statistical tests, to improve detection of alarming events.