TY  - GEN
N2  - Sleep-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.
DO  - 10.6083/h702q723n
DO  - DOI
AB  - Sleep-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.
T1  - Sleep signal processing for disordered breathing event detection and severity estimation
DA  - 2020
AU  - Snider, Brian R.
L1  - https://digitalcollections.ohsu.edu/record/8573/files/Snider.Brian.2020.pdf
PB  - Oregon Health and Science University
PY  - 2020
ID  - 8573
L4  - https://digitalcollections.ohsu.edu/record/8573/files/Snider.Brian.2020.pdf
KW  - Polysomnography
KW  - Deep Learning
KW  - Sleep Apnea Syndromes
KW  - Machine Learning
KW  - computer-assisted signal processing
KW  - computer neural network
TI  - Sleep signal processing for disordered breathing event detection and severity estimation
Y1  - 2020
L2  - https://digitalcollections.ohsu.edu/record/8573/files/Snider.Brian.2020.pdf
LK  - https://digitalcollections.ohsu.edu/record/8573/files/Snider.Brian.2020.pdf
UR  - https://digitalcollections.ohsu.edu/record/8573/files/Snider.Brian.2020.pdf
ER  -