A machine learning eye movement detection algorithm using electrooculography

Abstract Study Objectives Eye movement quantification in polysomnograms (PSG) is difficult and resource intensive. Automated eye movement detection would enable further study of eye movement patterns in normal and abnormal sleep, which could be clinically diagnostic of neurologic disorders, or used...

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Veröffentlicht in:	Sleep (New York, N.Y.) N.Y.), 2023-04, Vol.46 (4), p.1
Hauptverfasser:	Dupre, Alicia E, Cronin, Michael F M, Schmugge, Stephen, Tate, Samuel, Wack, Audrey, Prescott, Brenton R, Li, Cheyi, Auerbach, Sanford, Suchdev, Kushak, Al-Faraj, Abrar, He, Wei, Cervantes-Arslanian, Anna M, Abdennadher, Myriam, Saxena, Aneeta, Lehan, Walter, Russo, Mary, Pugsley, Brian, Greer, David, Shin, Min, Ong, Charlene J
Format:	Artikel
Sprache:	eng
Schlagworte:	Algorithms Analysis Automation Brain Electrooculography - methods Eye Movements Humans Injuries Machine Learning Nervous system diseases Neural networks Neurophysiology REM sleep Retrospective Studies Sleep Sleep disorders
Online-Zugang:	Volltext
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