Sleep scoring involves classification of polysomnographic data into the various sleep stages as defined by Retschaffen and Kales. This process is time-consuming and laborious as it involves experts visually scoring the data. During recent years, there has been an increasing focus on automated sleep scoring systems and professional software programs are finding increased use. However, these systems are not relied on for scoring and are often used as a tool that facilitates easy visual scoring. This thesis proposes a neural network based approach to automatic sleep scoring using LabVIEW. Effort has been made to give the sleep expert more control over key parameters such as the frequency bands, and thus come up with scores that are more in agreement with the individual scorer than being a rigid interpretation of the R&K rules. Though this thesis is limited to the development of an offline software program, given the data acquisition facilites in LabVIEW, a complete system from data acquisition to sleep hypnograms is a fair possibility.

One of the important topics in sleep medicine is sleep structure. Normal sleep consists of rapid eye movement (REM) sleep and nonrapid eye movement (NRME) sleep states. NREM sleep can be further classified into stage 1, 2 and slow wave sleep (SWS) according to the current sleep scoring standard. Among them, SWS has been considered to be very important due to its r restorative value. The goal of this research is to detect SWS by using a single channel EEG signal. Its applications can be divided into two phases. In the first phase, a personalized SWS detector is designed for each individuals By combining these personalized SWS detectors, the second phase develops a general SWS detection method that can be applied to general population with any personalized training process. By applying the proposed method to 62 persons, the experimental results show that the proposed method, in average, achieves 90.69% classification accuracy 90.09% sensitivity and 93.97% specificity. Our experimental results also demonstrate, when applied to persons with higher AHI (apnoea-hypopnea index) values, the proposed method can still provided satisfactory results.

Sleep Recording is important for clinical diagnosis and treatment of sleep disorders. Sleep staging is one of the most important steps in sleep analysis and is typically performed based on the characteristics of electrophysiological signals including EEG, EOG, and EMG. Normal healthy sleep consists of sequences of stages. According to the traditional Rechtschaffen & Kales (R&K) rules, these stages include: Awake, Light Sleep, Deep Sleep, and Rapid-Eye Movement (REM) Sleep. Our study develops a simple four-stage process to classify sleep into wakefulness, stage 1, stage 2, slow wave sleep (SWS) and rapid eye movement (REM) sleep based on single LEOG channel. To achieve this goal, this study first generates feature variables from LEOG signal. The proposed feature selection method is applied to select a subset of features to improve the accuracy of the classifier. By applying the proposed approach to 48727 distinct LEOG epochs that are obtained from 62 subjects, the accuracy rate is about 72.6%. The largest amount of errors occurs in the identification of Stage 1, 56.3% of which was misclassified into stages 2 or wake. The second largest error is associated with REM sleep, 23.7% of which was misclassified into stages 2.

This study aims at sleep stage classification problem via EOG signals. The classification problem consists of four steps. The first step is to distinguish slow wave sleep from the rest of the sleep periods. Wake periods are identified in the second step. The third step finds REM sleep and the last step classifies stage 2 and stage1 sleep. By using different EOG signal features in different steps of the classification process, this work uses back-propagation trained neural networks to perform classification. With the exception of stage 1 sleep, the sensitivity and positive predictive value ranges from 70% to 80%. The overall classification accuracy is 74.80%.

Thesis (Ph. D.)--Washington State University, December 2008. / Title from PDF title page (viewed on Sept. 23, 2008). "Department of Psychology." Includes bibliographical references (p. 48-56).

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 55-59). Also available on the Internet.

Thesis (M.P.H.)--University of Hong Kong, 2009. / Includes bibliographical references (p. 37-42).

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Nita Lewis Miller. Includes bibliographical references (p. 77-81). Also available online.

Thesis (M.S. in Operations Research)--Naval Postgraduate School, June 2004. / Thesis advisor(s): Laura A. Barton. Includes bibliographical references (p. 53-55). Also available online.

Thesis (Ph. D.)--University of Missouri-Columbia, 2001. / Typescript. Vita. Includes bibliographical references (leaves 55-59). Also available on the Internet.