Supporting diagnosis of attention-deficit hyperactive disorder with novelty detection

摘要

ObjectiveComputerized continuous performance test (CPT) is a widely used diagnostic tool for attention-deficit hyperactivity disorder (ADHD). It measures the number of correctly detected stimuli as well as response times. Typically, when calculating a cut-off score for discriminating between normal and abnormal, only the normal children’s data are collected. Then the average and standard deviation of each measure or variable is computed. If any of variables is larger than 2 sigma above the average, that child is diagnosed as abnormal. We will call this approach as “T-score 70” classifier. However, its performance has a lot to be desired due to a high false negative error. In order to improve the classification accuracy we propose to use novelty detection approaches for supporting ADHD diagnosis.