Fetal heart rate signal processing

A device for training a fetal heart rate anomaly detection, comprising:

• a data interface for accessing a training dataset comprising multiple fetal heart rate signals of fetuses and corresponding uterine activity signals;
• a processor subsystem configured to:
• using the fetal heart rate signals and corresponding uterine activity signals of the training dataset, train a fetal heart rate prediction model to, based on a representation of a first fetal heart rate segment, a representation of a corresponding first uterine activity segment, and a representation of a subsequent second uterine activity segment, predict a representation of a second fetal heart rate segment;
• output the fetal heart rate prediction model.

A computer-implemented method of processing a fetal heart rate signal, comprising:

• accessing a fetal heart rate signal of a fetus and a corresponding uterine activity signal;
• accessing model data representing a fetal heart rate prediction model configured to, based on an encoding of a first fetal heart rate segment, an encoding of a first uterine activity segment, and an encoding of a second uterine activity segment, predict an encoding of a second fetal heart rate segment;
• obtaining a first fetal heart rate segment and a subsequent second fetal heart rate segment of the fetal heart rate signal;
• obtaining a first uterine activity segment and a second uterine activity segment of the uterine activity signal, corresponding respectively to the first and second fetal heart rate segments;
• applying the fetal heart rate prediction model to a representation of the first fetal heart rate segment, a representation of the first uterine activity segment, and a representation of the second uterine activity segment to obtain a predicted representation of the second fetal heart rate segment;
• determining an anomaly score indicative of fetal health by comparing the predicted representation of the second fetal heart rate segment to the obtained second fetal heart rate segment, and outputting the anomaly score.