Projects:2017s1-157 Automated Classification of Brain Activity during Sleep

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Introduction

The aim of this project is to create an automated sleep stage classifier that identifies various stages of sleep based on a test that is recorded via electroencephalogram (EEG). An algorithm will be developed to classify large sets of overnight sleep data into sleep stages. The aim of the project is to achieve an accuracy of 85% when comparing the algorithm results to manual classification.


Project Description

Sleep is a state of reduced consciousness that can be classified into different stages, based on the level of brain activity as recorded via electroencephalogram (EEG). The aim of this project is to develop a automated sleep staging algorithm and validate on a large data set of overnight seep studies, using Matlab. An interest in signal processing, feature extraction and classification as well as sleep is required.



Project Group Members:

Woojin Jung

Brock Hamann


Project Supervisors

Mathias Baumert

Sarah Anita Immanuel

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