DSleepNet: Disentanglement Learning for Personal Attribute-Agnostic Three-Stage Sleep Classification Using Wearable Sensing Data
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DOI: 10.1109/jbhi.2025.3541851
Abstract: Long-term non-invasive sleep stage monitoring is instrumental in comprehending the progression of sleep disorders, cardiovascular diseases, and the interplay between sleep, type 2 diabetes, and neurodegenerative diseases. However, the conventional deep learning approach is susceptible… read more here.
Keywords: personal attribute; dsleepnet disentanglement; stage; attribute agnostic ... See more keywords