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Related Concept Videos

Sleep-Wake Cycles

Sleep-Wake Cycles

Sleep is an essential physiological process vital to maintaining overall well-being. The reticular activating system (RAS), a network of neurons in the brainstem, regulates wakefulness and sleep. While it may seem passive, sleep consists of distinct cycles, each with its unique characteristics and functions. Two key sleep phases are non-rapid eye movement (NREM) and rapid eye movement (REM).
NREM Sleep
NREM sleep comprises four progressive stages that seamlessly merge:

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Updated: Jun 18, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Validating CircaCP: a generic sleep-wake cycle detection algorithm for unlabelled actigraphy data.

Shanshan Chen¹, Xinxin Sun¹

¹Department of Biostatistics, School of Population Health, Virginia Commonwealth University, Richmond, VA, USA.

Royal Society Open Science

|July 30, 2024

Summary

This summary is machine-generated.

This study introduces CircaCP, an unsupervised algorithm for sleep-wake cycle detection from actigraphy. It accurately estimates sleep timing, showing broad applicability across different sensors and populations.

Keywords:

actigraphy external validation parametric change point detection unsupervised approach

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Last Updated: Jun 18, 2025

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments

Published on: August 8, 2019

Multi-Modal Home Sleep Monitoring in Older Adults

Multi-Modal Home Sleep Monitoring in Older Adults

Published on: January 26, 2019

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Human Circadian Phenotyping and Diurnal Performance Testing in the Real World

Published on: April 7, 2020

Area of Science:

Biomedical Engineering
Chronobiology
Data Science

Background:

Accurate sleep-wake cycle detection is crucial for analyzing actigraphy data.
Existing supervised learning algorithms often lack generalizability across different sensors and studies.
Unsupervised methods offer a potential solution for robust sleep-wake detection.

Purpose of the Study:

To introduce and validate CircaCP, an unsupervised algorithm for detecting sleep-wake cycles from actigraphy.
To assess the generalizability of CircaCP across different sensors and age groups.
To provide a reliable method for extracting temporal sleep metrics.

Main Methods:

Developed CircaCP, an unsupervised algorithm utilizing a cosinor model for circadian rhythm estimation and change point analysis for sleep-wake detection.
Applied CircaCP to estimate sleep/wake onset times (S/WOTs) from 2125 individuals' actigraphy data (MESA sleep study).
Validated estimated S/WOTs against self-reported event markers using Bland-Altman and variance component analyses.

Main Results:

CircaCP demonstrated high accuracy, with sleep onset times (SOTs) averaging 3.6 minutes behind reported markers and wake onset times (WOTs) less than 1 minute behind.
The algorithm's estimations accounted for minimal variability (<0.2%) in S/WOTs, indicating robustness.
CircaCP showed seamless transferability from children's hip-worn ActiGraph data to adults' wrist-worn Actiwatch data.

Conclusions:

CircaCP is a validated, unsupervised algorithm for accurate sleep-wake cycle detection from actigraphy.
The algorithm's strong generalizability across sensors and populations makes it widely applicable.
CircaCP offers a reliable and robust method for extracting temporal sleep metrics from diverse actigraphy datasets.