Optimizing wearable single-channel electroencephalography sleep staging in a heterogeneous sleep-disordered population using transfer learning.

ABSTRACT:

Study objectives

Although various wearable electroencephalography devices have been developed, performance evaluation of the devices and their associated automated sleep stage classification models is mostly limited to healthy participants. A major barrier for applying automated wearable electroencephalography sleep staging in clinical populations is the need for large-scale data for model training. We therefore investigated transfer learning as a strategy to overcome limited data availability and optimize automated single-channel electroencephalography sleep staging in people with sleep disorders.

Methods

We acquired 52 single-channel frontopolar headband electroencephalography recordings from a heterogeneous sleep-disordered population with concurrent polysomnography (PSG)

SUBMITTER: van der Aar JF 

PROVIDER: S-EPMC11789263 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature