Reduction Of Classification Error Rates And Monitoring System Using An Artificial Class
First Claim
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1. A seizure advisory system comprising a user interface, the user interface comprising:
- an output interface configured to provide an indication corresponding to a result of classification of data from a subject; and
an output interface configured to provide an indication of uncertainty when a result of classification of data from a subject is indicative of uncertainty in the classification.
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Abstract
Systems and methods for enhancing the accuracy of classifying a measurement by providing an artificial class. Seizure prediction systems may employ a classification system including an artificial class and a user interface for signaling uncertainty in classification when a measurement is classified in the artificial class.
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Citations
12 Claims
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1. A seizure advisory system comprising a user interface, the user interface comprising:
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an output interface configured to provide an indication corresponding to a result of classification of data from a subject; and an output interface configured to provide an indication of uncertainty when a result of classification of data from a subject is indicative of uncertainty in the classification. - View Dependent Claims (2, 3, 4, 5, 6, 7, 8)
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9. A seizure advisory system comprising:
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at least one sensor input that receives at least one electrical signal indicative of brain activity; a processing unit configured to determine a measurement feature vector based at least in part on the at least one electrical signal; a classifier comprising a plurality of empirical classes, each empirical class associated with at least one feature vector from a training set of feature vectors and being associated with an estimated neural condition and an other class independent of each empirical class; and an output device for communicating information related to a result of applying the classifier to the measurement feature vector. - View Dependent Claims (10, 11, 12)
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Specification