Abstract: A method and apparatus for monitoring the condition of a fetus to assess a degree of risk of developing a permanent neurological condition is provided. A signal indicative of a fetal heart is processed to derive a degree of risk of developing a permanent neurological condition. The data indicative of the degree of risk of developing a permanent neurological condition indicates a likelihood that the condition of the fetus belongs to a class in a group of classes, where each class in the group of classes is associated with a pre-defined fetal condition. Data indicative of the degree of risk of developing a permanent neurological condition is then released. In one example, a neural network is used to obtain data indicating the likelihood that the condition of the fetus belongs to a class in the group of classes.

Abstract: A system and apparatus implementing a graphical user interface for displaying heart rate information is provided. The graphical user interface displays, in a first viewing window, a first tracing indicative of a heart rate signal. The graphical user interface displays a control allowing a user to select a portion of the first tracing. In a second viewing window displayed simultaneously with the first viewing window, the graphical user interface displays a second tracing which is a zoomed in view of the selected portion of the first tracing. In a specific implementation, the control includes a selection box having a transparent portion superposed upon the first viewing window. The portion of the first tracing viewable through the transparent portion corresponds to the second tracing. The control allows the user to displace and modify the size of the selection box to select a portion of the first tracing.

Abstract: A method and apparatus for segmenting a heart rate signal to identify heart rate feature events are provided. A heart rate signal including a sequence of sample points is received. The heart rate signal is processed to generate a set of segments. Each segment is formed by enclosing a portion of the heart rate signal in a respective bounded area commencing at a start sample point and terminating at an end sample point of the heart rate signal. The sample points between the start sample point and end sample point lie within the bounded area. The set of segments are then processed to generate a plurality of sections, each section being indicative of a heart rate feature. The heart rate feature is selected from the set consisting of an acceleration event, a deceleration event and a baseline event. A signal indicative of the plurality of sections is then released.

Abstract: A system and apparatus implementing a graphical user interface for displaying heart rate information is provided. The graphical user interface displays, in a first viewing window, a first tracing indicative of a heart rate signal. The graphical user interface displays a control allowing a user to select a portion of the first tracing. In a second viewing window displayed simultaneously with the first viewing window, the graphical user interface displays a second tracing which is a zoomed in view of the selected portion of the first tracing. In a specific implementation, the control includes a selection box having a transparent portion superposed upon the first viewing window. The portion of the first tracing viewable through the transparent portion corresponds to the second tracing. The control allows the user to displace and modify the size of the selection box to select a portion of the first tracing.

Abstract: A method and apparatus for monitoring the condition of a fetus to assess a degree of risk of developing a permanent neurological condition is provided. A signal indicative of a fetal heart is processed to derive a degree of risk of developing a permanent neurological condition. The data indicative of the degree of risk of developing a permanent neurological condition indicates a likelihood that the condition of the fetus belongs to a class in a group of classes, where each class in the group of classes is associated with a pre-defined fetal condition. Data indicative of the degree of risk of developing a permanent neurological condition is then released. In one example, a neural network is used to obtain data indicating the likelihood that the condition of the fetus belongs to a class in the group of classes.