Towards a Flexible Deep Learning Method for Automatic Detection of Clinically Relevant Multi-Modal Events in the Polysomnogram

16 May 2019Alexander Neergaard OlesenStanislas ChambonValentin ThoreyPoul JennumEmmanuel MignotHelge B. D. Sorensen

Much attention has been given to automatic sleep staging algorithms in past years, but the detection of discrete events in sleep studies is also crucial for precise characterization of sleep patterns and possible diagnosis of sleep disorders. We propose here a deep learning model for automatic detection and annotation of arousals and leg movements... (read more)

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