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Temporal convolutional networks and transformers for classifying the sleep stage in awake or asleep using pulse oximetry signals
In this work, we developed a network architecture with the aim of classifying the sleep stage in awake or asleep using only HR signals from a pulse oximeter.
Classifying sleep-wake stages through recurrent neural networks using pulse oximetry signals
The regulation of the autonomic nervous system changes with the sleep stages causing variations in the physiological variables.
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