Reduction Of Classification Error Rates And Monitoring System Using An Artificial Class

1 . A method of analyzing a physiological signal using a computing system, comprising:

receiving at a computing system subject data representing sampled physiological signal measured from a subject;

deriving using the computing system a feature vector from the subject data;

receiving classification information from a reviewer, said classification information identifying an empirical class associated with a segment of the subject data and representing an empirically-determined condition;

generating an artificial class not representing the empirically-determined condition; and

classifying a feature vector from subsequent subject data representing sampled physiological signals measured from the subject into the empirical class or the artificial class.

2 . The method of claim 1 , wherein said artificial class captures feature vectors atypical of any empirically-determined conditions.

3 . The method of claim 1 , wherein said artificial class captures feature vectors near a boundary between a first empirical class and a second empirical class.

4 . The method of claim 1 , wherein said artificial class captures feature vectors associated with an unknown condition.

5 . The method of claim 1 , wherein said artificial class captures feature vectors determined to be particularly susceptible to misclassification.

6 . The method of claim 1 , further comprising displaying on a computer user interface the classification of the feature vector from subsequent subject data.

7 . The method of claim 1 , wherein said sampled physiological signal comprises a sampled neurological signal.

8 . The method of claim 7 , wherein said empirically-determined condition comprises a seizure state.