Performance of an open machine learning model to classify sleep/wake from actigraphy across ∼24-hour intervals without knowledge of rest timing.

ABSTRACT:

Goal and aims

Commonly used actigraphy algorithms are designed to operate within a known in-bed interval. However, in free-living scenarios this interval is often unknown. We trained and evaluated a sleep/wake classifier that operates on actigraphy over ∼24-hour intervals, without knowledge of in-bed timing.

Focus technology

Actigraphy counts from ActiWatch Spectrum devices.

Reference technology

Sleep staging derived from polysomnography, supplemented by observation of wakefulness outside of the staged interval. Classifications from the Oakley actigraphy algorithm were additionally used as performance reference.

Sample

Adults, sleeping in either a home or laboratory environment.

Design

Machine learning was used to train and evaluate a sleep/wake classifi

SUBMITTER: Roberts DM 

PROVIDER: S-EPMC11005467 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature