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Detection of Sleep Oxygen Desaturations from Electroencephalogram Signals
Development of a machine learning technique which can successfully identify EEG signals from patients with sleep apnea as well as identify latent EEG signals which come from subjects who experience oxygen desaturations but do not themselves occur during oxygen desaturation events would provide a strong step towards developing a brain-based biomarker for sleep apnea in order to aid with easier diagnosis of this disease.
Impact of Physical Activity on Sleep:A Deep Learning Based Exploration
In this paper we explore the use of deep learning to build sleep quality prediction models based on actigraphy data.
Robust Automated Human Activity Recognition and its Application to Sleep Research
Human Activity Recognition (HAR) is a powerful tool for understanding human behaviour.
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