Two approaches to the automatic analysis of clinical electroencephalograms
(EEGs) are considered with a view towards classifying clin ical EEGs as normal or abnormal. The first approach examines the variability
of various EEG features in a population of astronaut candidates known to be free of neurological disorders by constructing histograms of these features; unclassified EEGs of subjects in the same age group are examined by comparison of their feature values to the histograms of this neurologically normal group. The second approach employs the techniques of automatic pattern recognition for classification of clinical EEGs. A set of 57 EEG records designated normal or abnormal by clinical electro-encephalographers are used to evaluate pattern recognition systems based on stepwise discriminant analysis. In particular, the efficacy of using various feature sets in such pattern recognition systems is evaluated in terms of estimated classification error probabilities (Pe). The results of the study suggest a potential for the development of satisfactory automatic systems for the classification of clinical EEGs. / Applied Science, Faculty of / Electrical and Computer Engineering, Department of / Graduate
| Identifer | oai:union.ndltd.org:UBC/oai:circle.library.ubc.ca:2429/19111 |
| Date | January 1974 |
| Creators | Doyle, Daniel John |
| Source Sets | University of British Columbia |
| Language | English |
| Detected Language | English |
| Type | Text, Thesis/Dissertation |
| Rights | For non-commercial purposes only, such as research, private study and education. Additional conditions apply, see Terms of Use https://open.library.ubc.ca/terms_of_use. |
Page generated in 0.0014 seconds