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Bachelor project

Early diagnosis of neurodegenerative diseases is vital in order to provide treatment and inhibit progression at the early stages. In that relation, it has been suggested that the K-complex (KC) density in sleep EEG might be correlated with a predisposition for neurodegenerative diseases.
In order to be able to investigate such a correlation, it is necessary to have a functional and reliable KC detection algorithm, as manual KC annotation is an extensive job.
In this project we developed a semi-automatic K-Complex detection algorithm, using wavelet transformation to identify pseudo- K-Complexes and various feature thresholds to reject false positives.
The algorithm was trained and tested on sleep EEG from two databases to enhance its general applicability.
StatusCompleted
Period01/02/201330/08/2014
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ID: 150060245