000008573 001__ 8573
000008573 005__ 20240124114303.0
000008573 0247_ $$2DOI$$a10.6083/h702q723n
000008573 037__ $$aETD
000008573 245__ $$aSleep signal processing for disordered breathing event detection and severity estimation
000008573 260__ $$bOregon Health and Science University
000008573 269__ $$a2020
000008573 336__ $$aDissertation
000008573 502__ $$bPh.D.
000008573 520__ $$aSleep-disordered breathing (SDB) is recognized as a widespread, under-diagnosed condition associated with many detrimental health problems. The condition places a significant burden on the individual and the healthcare system alike, with untreated SDB patients utilizing national health resources at twice the usual rate. The most common form of SDB is obstructive sleep apnea, characterized by frequent transient reductions of oxygen saturation, cessations of ventilatory airflow, and collapse or obstruction of the upper airway. Other forms of SDB include hypopnea, characterized by a reduction of ventilatory airflow; central apnea, with a cessation of ventilatory effort and airflow; and mixed apnea, a combination of central and obstructive apnea.
000008573 542__ $$fIn copyright - single owner
000008573 650__ $$aPolysomnography$$029845
000008573 650__ $$aDeep Learning$$012734
000008573 650__ $$aSleep Apnea Syndromes$$026067
000008573 650__ $$aMachine Learning$$011449
000008573 6531_ $$acomputer-assisted signal processing
000008573 6531_ $$acomputer neural network
000008573 691__ $$aSchool of Medicine$$041369
000008573 7001_ $$aSnider, Brian R.
000008573 8564_ $$98afddb3e-f0a4-41b4-8c31-90f5d8da1ea7$$s11181968$$uhttps://digitalcollections.ohsu.edu/record/8573/files/Snider.Brian.2020.pdf
000008573 905__ $$a/rest/prod/h7/02/q7/23/h702q723n
000008573 909CO $$ooai:digitalcollections.ohsu.edu:8573$$pstudent-work
000008573 980__ $$aTheses and Dissertations