Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: Systems and methods for automatically identifying segments of EEG signals or other brain electrical activity signals that contain artifacts, and/or editing the signals to remove segments that include artifacts.

Abstract: A method of building classifiers for neurological assessment is described. The method comprises the steps of extracting quantitative features from a plurality of clinical features, and selecting a subset of features from the extracted pool of features to construct binary classifiers. A device for performing point-of-care neurological assessment using clinical features is also described.

Abstract: Systems and methods for automatically identifying segments of EEG signals or other brain electrical activity signals that contain artifacts, and/or editing the signals to remove segments that include artifacts.

Abstract: Systems and methods for automatically identifying segments of EEG signals or other brain electrical activity signals that contain artifacts, and/or editing the signals to remove segments that include artifacts

Abstract: A method and apparatus for providing an objective assessment of the neurological state of a patient using a field-portable neuro-assessment device is described. The method includes placing an electrode set on the patient's head, acquiring spontaneous brain electrical signals and evoked potential signals from the patient through the electrode set, processing the signals using a handheld base unit, and displaying a result indicating the probability of the patient's neurological signal being normal or abnormal. The neuro-assessment device allows for a rapid, on-site neurological evaluation by an emergency medical technician, triage nurse, or any other medical personnel to identify patients with neurological disorders who may require immediate medical attention.

Abstract: A system and method for prediction of cognitive decline, comprises an input receiving input data corresponding to brain activity of an individual and a processor coupled to the input for analyzing the input data to obtain a selected set of features, the processor comparing the selected set of features to at least a portion of entries in a database corresponding to brain activity of a plurality of individuals, wherein entries in the database have been separated into a plurality of categories corresponding to one of a degree of cognitive decline and a propensity for future cognitive decline of individuals relating to the entries, the processor determining, based on the comparison, a category most closely corresponding to the selected set of features.

Abstract: Described is a system and method which includes the following steps. A plurality of interventional agents are administered to a patient until the patient attains a predetermined plane of anesthesia. Brain waves of the patient are amplified and digitized before and after the administering step to generate a first set of digital data. The brain waves of the patient are amplified and digitized during a medical procedure to generate a second set of digital data. Separate trajectories for at least two different indices of an anesthetic state of the patient are computed during the medical procedure as a function of a comparison of the first and second sets of digital data. The indices include a Depth Index (DI), a Memory Index (MI) and a Pain Index (PI). The DI corresponds to a depth of anesthesia of the patient, the PI corresponds to a sensitivity of the patient to pain and the MI corresponds to an ability of the patient to form and store memories.