Abstract: The present invention provides a method and apparatus for monitoring the fetal condition in an objective, quantitative, and non-invasive manner by enabling continuous and highly specific data collection during pre-natal development and the intra-partum period. In an embodiment, monitoring the fetal condition comprises monitoring a fetal heart rate, a fetal heart rate variability, and/or the fetal heart vector orientation of a fetus in a pregnant mother. Automated signal processing techniques are applied to a multiplicity of transducers to select an optimum signal vector to monitor at least one of a fetal heart rate, a fetal heart rate variability, and/or the fetal heart vector orientation of a fetus in a pregnant mother.

Abstract: The present invention provides a method and apparatus for monitoring the fetal condition in an objective, quantitative, and non-invasive manner by enabling continuous and highly specific data collection during pre-natal development and the intra-partum period. In an embodiment, monitoring the fetal condition comprises monitoring a fetal heart rate, a fetal heart rate variability, and/or the fetal heart vector orientation of a fetus in a pregnant mother. Automated signal processing techniques are applied to a multiplicity of transducers to select an optimum signal vector to monitor at least one of a fetal heart rate, a fetal heart rate variability, and/or the fetal heart vector orientation of a fetus in a pregnant mother.

Abstract: A method of developing a controller for general (nonlinear) discrete-time systems, where the equations governing the system are unknown and where a controller is estimated without building or assuming a model for the system. The controller is constructed through the use of a function approximator (FA) such as a neural network or polynomial. This involves the estimation of the unknown parameters within the FA through the use of a stochastic approximation that is based on a simultaneous perturbation gradient approximation, which requires only system measurements (not a system model).

Abstract: A patient monitoring system and a method of operating same, including automatically detecting a seizure in the patient, by detecting an electrical discharge in the patient's brain; converting the detected electrical discharge into a digital signal; inputting the digital signal into a microprocessor; detecting a seizure by dividing the digital signals into time segments, preprocessing each time segment including standardizing the signal, reducing the signal in each time segment to a feature, the feature providing information about whether a seizure is occurring using the feature from each time segment; and indicating that a seizure is occurring.