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http://hdl.handle.net/1942/8967
Title: | Classification methods multi-class multivariate longitudinal data | Authors: | WOUTERS, Kristien | Advisors: | MOLENBERGHS, Geert CORTINAS ABRAHANTES, Jose |
Issue Date: | 2008 | Publisher: | UHasselt Diepenbeek | Abstract: | In this thesis, we have focussed on the classification of multiple class, multivariate longitudinal data. This research was driven by a study conducted to classify psychotropic drugs based on electro-encephalogram or EEG data. For each of the compound-by-dose combinations in the five psychotropic drug classes, data on the sleep-wake behaviour of rats were collected during a 16 hours period. The sleepwake behaviour was summarized into six standard sleep-wake stages, resulting in a six-variate longitudinal profile per rat. From a statistical point of view, analyzing EEG data poses important challenge, because of the high-dimensionality and the longitudinal character of the data. The longitudinal profiles are usually highly irregular and the variability between and within subjects are relatively high. | Document URI: | http://hdl.handle.net/1942/8967 | Category: | T1 | Type: | Theses and Dissertations |
Appears in Collections: | PhD theses Research publications |
Files in This Item:
File | Description | Size | Format | |
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Kristien Wouters.pdf | 1.69 MB | Adobe PDF | View/Open |
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