This project presents the study to empirically evaluate the ability of LSTMs to recognize patterns in multivariate time series of clinical measurements during childbirth that mainly consist of two vital parameters FHR and UC. Specifically, it considers binary classification for diagnosis and prior detection of Fetal Distress before and during childbirth. The proposed solution employs a novel architecture consisting of signal resampling and multiple stacked LSTMs.
