Abstract
Principal component analysis enhanced by the use of smoothing is used in conjunction with discriminant analysis techniques to devise a statistical classification method for the analysis of event-related potential data. A training set of premedication potentials collected from adolescents with attention-deficit hyperactive disorder (ADHD) is used to predict whether adolescents from an independent subject group will respond to long-term medication. Comparison of outcome prediction rates demonstrates that this method, which uses information from the whole ERP curve, is superior to the classification technique currently used by clinicians, which is based on a single ERP curve feature. The need to administer an initial dose of medication to classify patients is also eliminated.
Original language | English (US) |
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Pages (from-to) | 174-181 |
Number of pages | 8 |
Journal | Biometrics |
Volume | 55 |
Issue number | 1 |
DOIs | |
State | Published - Mar 1999 |
Keywords
- Attention-deficit disorder
- Auditory evoked potentials
- Classification
- Discriminant analysis
- Functional analysis
- Principal components
- Smoothing
ASJC Scopus subject areas
- Statistics and Probability
- General Biochemistry, Genetics and Molecular Biology
- General Immunology and Microbiology
- General Agricultural and Biological Sciences
- Applied Mathematics