Automatic Sleep Scoring To Study Brain Resting State Networks During Sleep In Narcoleptic And Healthy Subjects: A Combination Of A Wavelet Filter Bank And An Artificial Neural Network
Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Manual sleep scoring, executed by visual inspection of the EEG, is a very time consuming activity, with an inherent subjective decisional component. Automatic sleep scoring could ease the job of the technicians, because faster and more accurate. Frequency information characterizing the main brain rhythms, and consequently the sleep stages, needs to be extracted from the EEG data. The approach used in this study involves a wavelet filter bank for the EEG frequency features extraction. The wavelet packet analysis tool in MATLAB has been employed and the frequency information subsequently used for the automatic sleep scoring by means of an artificial neural network. Finally, the automatic sleep scoring has been employed for epoching the fMRI data, thus allowing for studying brain resting state networks during sleep. Three resting state networks have been inspected; the Default Mode Network, The Attentional Network and the Salience Network. The networks functional connectivity variations have been inspected in both healthy and narcoleptic subjects. Narcolepsy is a neurobiological disorder characterized by an excessive daytime sleepiness, whose aetiology may be linked to a loss of neurons in the hypothalamic region.
Place, publisher, year, edition, pages
2014. , 89 p.
Automatic Sleep Scoring, Discrete Wavelet Transform, Filter Bank, Artificial Neural Network, Resting State Networks, Narcolepsy.
Engineering and Technology Signal Processing Medical Engineering Neurology
IdentifiersURN: urn:nbn:se:liu:diva-110950ISRN: LIU-IMH/RV-A--14/002—SEOAI: oai:DiVA.org:liu-110950DiVA: diva2:751134
Subject / course
Master's Program Biomedical Engineering
van Ettinger-Veenstra, Helene, PhD
Engström, Maria, Associate Professor