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dc.contributor.advisorMetsis, Vangelis
dc.contributor.authorBiniwale, Alakh Sudhir ( )
dc.date.accessioned2017-01-06T18:02:43Z
dc.date.available2017-01-06T18:02:43Z
dc.date.issued2016-08
dc.identifier.citationBiniwale, A. S. (2016). Analysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://digital.library.txstate.edu/handle/10877/6410
dc.description.abstractThis thesis work evaluates our proposed methodology for automated detection of sleep events from Polysomnographic (PSG) data. The sleep data was collected during real sleep studies using Profusion PSG3. The event detection tasks used a Hidden Markov Model (HMM) to achieve signal classification for sleep event detection. The Hilbert transform (envelope) was used to extract features for input to the HMM. HMM was selected as our classification method of choice, due to the fact that it was able to capture the temporal variations of the biosignals collected through PSG. In this work, we detected sleep motion events, such as rapid eye movements (REM) and leg movements, and breathing events like obstructive apnea, hypopnea and snore. The task of detecting events of interest was achieved using a sliding window approach, and classifying each signal segment as containing an event or not, hence, leading to a binary classification problem for each type of event. Our experimental results show that our proposed approach can be successfully used for sleep event detection, to assist experts in sleep quality assessment, however, the big imbalance between the number of segments that contain a positive event and the ones that do not, often negatively affects the performance of our classification method.
dc.formatText
dc.format.extent47 pages
dc.format.medium1 file (.pdf)
dc.language.isoen
dc.subjectSleep event
dc.subjectPolysomnograpy
dc.subjectHMM
dc.subject.lcshMarkov processesen_US
dc.subject.lcshSleep disordersen_US
dc.subject.lcshPolysomnographyen_US
dc.titleAnalysis of human polysomnography (PSG) for automatic sleep event detection using Hidden Markov Model
txstate.documenttypeThesis
dc.contributor.committeeMemberGuirguis, Mina
dc.contributor.committeeMemberGao, Byron
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Scienceen_US
thesis.degree.grantorTexas State Universityen_US
thesis.degree.levelMasters
thesis.degree.nameMaster of Science
txstate.departmentComputer Science


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