Mamba-based deep learning approach for sleep staging on a wireless multimodal wearable system without electroencephalography.

The marginally significant ( p = . 0449) association between AHI and accuracy may owe to the fact that individuals with sleep apnea experience sleep fragmentation, resulting in N1 sleep, one of the most difficult stages to separate; nonetheless, the association was modest (R 2 = 0 . 0751).

also in 26,082 other papers