Abstract: A waveform processing system including a patient monitor, a display device, and a processor configured to control the display device to display a location in a stream of waveform data of a segment including a specified event such as aberration or variation in the waveform data, wherein the location of the segment is identified based on aggregating outputs of an AI model performing sliding window inferences on the stream of waveform data. Performing the sliding window inferences on the stream of waveform data may include dividing the waveform data into a plurality of overlapping chunks, each chunk comprising consecutive data values extracted from the stream of waveform data over a specified duration, and inputting each chunk into the AI model to generate arrays of output values indicating a presence or absence the segment, which may then be aggregated.

Abstract: Techniques are described for performing fetal heart rate (FHR) analytics using machine learning techniques. According to an embodiment, computer-implemented method comprises training a machine learning model using a supervised machine learning process to identify patterns in training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data. The method further comprises receiving new cardiotocograph data for a fetus and mother in real-time over a period of labor and applying the machine learning model to the new cardiotocograph data as it is received to identify the patterns in the new cardiotocograph data.

Abstract: One or more systems, devices, computer-implemented methods and/or computer program products of use provided herein relate to artificial intelligence (AI) to generate labor and delivery-based predictions. A system can comprise a processor that can execute computer-executable components stored in memory, wherein the computer-executable components can comprise a first AI model that can generate first data comprising one or more labor and delivery predictions applicable to one or more fetuses and a mother of the one or more fetuses, during labor, by analyzing second data comprising cardiotocography (CTG) analysis data of the one or more fetuses and the mother generated by a second AI model and third data comprising maternal health analysis data of the mother generated by a third AI model, wherein the first AI model can be a multistage AI model comprising respective models directed to predicting respective ones of the one or more labor and delivery predictions.

Abstract: Techniques are described for performing fetal heart rate (FHR) analytics using machine learning techniques. According to an embodiment, computer-implemented method comprises training a machine learning model using a supervised machine learning process to identify patterns in training cardiotocograph data that correspond to defined physiological events associated with respective fetuses and mothers of the fetuses represented in the training cardiotocograph data. The method further comprises receiving new cardiotocograph data for a fetus and mother in real-time over a period of labor and applying the machine learning model to the new cardiotocograph data as it is received to identify the patterns in the new cardiotocograph data.