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- W2941844008 abstract "Videosomnography (VSG) is a group of video-based methods used to record and label versus states in humans. Traditional behavioral-VSG (B-VSG) labeling requires visual inspection of the video by a trained technician to determine whether a subject is or awake. B-VSG is not used to label stages (e.g., slow wave or REM sleep), rather it solely labels whether a subject is asleep or at a particular time. In this paper we describe an automated VSG detection system which uses deep learning approaches to label frames in a video as sleep or awake in young children. We examine 3D Convolutional Networks (C3D) and Long Short-term Memory (LSTM) relative to motion information from selected Groups of Pictures of a video and test temporal window sizes for back propagation. We compared our proposed VSG methods to traditional B-VSG sleep-awake labels. C3D had an accuracy of approximately 90% and the proposed LSTM method improved the accuracy to more than 95%. The analyses revealed that estimates generated from the proposed LSTM-based method with long-term temporal dependency are suitable for automated or labeling." @default.
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- W2941844008 date "2019-03-01" @default.
- W2941844008 modified "2023-09-26" @default.
- W2941844008 title "Classification of Sleep Videos Using Deep Learning" @default.
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- W2941844008 doi "https://doi.org/10.1109/mipr.2019.00028" @default.
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