Jansen, Ben H.2016-08-182016-08-18May 20142014-05http://hdl.handle.net/10657/1411Objectives: We have developed a system to perform automated sleep staging. The system is a re implementation of the sleep staging method originally developed by Jansen in late 70’s. We extended this system by incorporating transient detection techniques. Method: The basic method divides the EEG into short intervals (about 1 s) from which autoregressive (AR) model coefficient are extracted. The AR coefficients serve as features in a clustering process to establish a library of elementary patterns. These are used to classify the 1 s EEG segments. Next, histograms showing how frequently each elementary pattern occurs in 30 s interval are obtained, and used for sleep staging. Special transient detection rules are then applied to detect spindles and rapid eye movements. Results: The method was developed using nine nights of sleep from three subjects and tested on sleep from seven other subjects. An overall classification rate of around 80 % was obtained. We observed that the method for spindle detection did not work as expected, but the REM detection method worked satisfactorily and it increased the classification percentage of stage 1 and REM. Conclusion: Acceptable sleep staging results were obtained, but improvement may be possible using better spindle and K-complex detection.application/pdfengThe author of this work is the copyright owner. UH Libraries and the Texas Digital Library have their permission to store and provide access to this work. Further transmission, reproduction, or presentation of this work is prohibited except with permission of the author(s).Signal processing2016-08-18Thesisborn digital