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</o:shapelayout></xml><![endif]--></head><body lang=EN-US link=blue vlink=purple><div class=WordSection1><p class=MsoNormal align=center style='text-align:center'><b><span style='font-size:16.0pt'>MS Thesis</span></b><span style='font-size:16.0pt'> <b>Announcement</b></span><o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'><b><span style='font-size:16.0pt'>Automated Sleep Staging</span></b><o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'>Nishant Prakash<o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'><b>Date: 29th April 2014</b><o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'><b>Place: ECE Conference Room</b><o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'><b>Time: 2.00 PM</b><o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'><b>Committee Chair: </b>Dr. Ben H. Jansen<o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'><b>Committee Members:</b><o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'>Dr. Bhavin Sheth<o:p></o:p></p><p class=MsoNormal align=center style='text-align:center'>Dr. Ahmet Omurtag<o:p></o:p></p><p class=MsoNormal style='text-align:justify'>Objectives: 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.<o:p></o:p></p><p class=MsoNormal style='text-align:justify'>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.<o:p></o:p></p><p class=MsoNormal style='text-align:justify'>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.<o:p></o:p></p><p class=MsoNormal style='text-align:justify'>Conclusion: Acceptable sleep staging results were obtained, but improvement may be possible using better spindle and K-complex detection.<o:p></o:p></p><p class=MsoNormal><o:p> </o:p></p></div></body></html>