A hybrid pre-processor for sleep staging using the E.E.G


Green, David Alan




A detailed survey concerning the analysis of normal human sleep by examination of electroencephalogram (EEG) data is presented. A critical review of methods which have been undertaken towards automating this process is included to justify the use of a hybrid system for high speed (1/60 real time) automated sleep analysis. Two varieties of feature detectors which form the basis of a hybrid system are then discussed in detail. The application of monolithic phase-locked loops for detection of two specific components of normal sleep (the alpha rhythm and sigma spindles) is described and a detector (delta rhythm) based on zero-crossing, amplitude measurement principles is also investigated. The accuracy of each of the three feature detectors is presented and their potential as elements of a hybrid sleep analysis system is illustrated by the analysis of two all-night EEG recordings. Finally, recommendations for the continuing development of the system are suggested.


Electrical engineering




Carleton University

Thesis Degree Name: 

Master of Engineering: 

Thesis Degree Level: 


Thesis Degree Discipline: 

Engineering, Electrical

Parent Collection: 

Theses and Dissertations

Items in CURVE are protected by copyright, with all rights reserved, unless otherwise indicated. They are made available with permission from the author(s).