An Accurate Sleep Staging System with Novel Feature Generation and Auto-Mapping

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An Accurate Sleep Staging System with Novel Feature Generation and Auto-Mapping
Title:
An Accurate Sleep Staging System with Novel Feature Generation and Auto-Mapping
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Proceedings of the 2017 International Conference on Orange Technologies (ICOT)
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08 December 2017
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Abstract:
Traditional sleep monitoring conducted in professional sleep labs and scored by sleep specialist is costly and labor intensive. Recent development of light-weight headband EEG provides possible solution for home-based sleep monitoring. This study proposed a machine learning approach for automatic sleep stage detection. A set of effective and efficient features are extracted from EEG data. The utilization of a collection of well annotated sleep data ensures the quality of learning model. A feature mapping algorithm is proposed to map the feature spaces generated from EEG data acquired through different electrodes.We collected headband EEG data for 1 hour naps in experiments conducted in our sleep lab. Preliminary result shows that sleep stages detected by proposed method are highly agreeable with the sleepiness score we obtained.
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