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  3. A Novel Machine Learning Unsupervised Algorithm For Sleep/wake Identification Using Actigraphy.
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  3. A Novel Machine Learning Unsupervised Algorithm For Sleep/wake Identification Using Actigraphy.

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A novel machine learning unsupervised algorithm for sleep/wake identification using actigraphy.

Xinyue Li1,2, Yunting Zhang2,3, Fan Jiang3,4

  • 1School of Data Science, City University of Hong Kong, Hong Kong, China.

Chronobiology International
|April 29, 2020

View abstract on PubMed

Summary
This summary is machine-generated.

A new unsupervised Hidden Markov Model (HMM) algorithm accurately identifies sleep/wake states from actigraphy data, outperforming existing methods and characterizing individual activity patterns for broader research applications.

Keywords:
ActigraphyHidden Markov Modelaccelerometerpattern recognitionrest-activity circadian rhythmsleep detectionunsupervised algorithm

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Area of Science:

  • Sleep science and biomedical engineering.
  • Machine learning applications in healthcare.

Background:

  • Actigraphy is a common tool for sleep studies, but lacks a universal unsupervised algorithm for sleep/wake identification.
  • Unsupervised algorithms are crucial for large-scale studies and when polysomnography (PSG) is unavailable, as they don't require pre-labeled data.

Purpose of the Study:

  • To propose and evaluate a novel unsupervised machine learning algorithm based on the Hidden Markov Model (HMM) for individualized sleep/wake identification using actigraphy.
  • To compare the performance of the HMM algorithm against existing unsupervised (Actiwatch Software) and supervised (UCSD) algorithms using PSG as the reference standard.

Main Methods:

  • Developed an individualized, unsupervised Hidden Markov Model (HMM) algorithm utilizing actigraphy data to infer sleep and wake states.
  • Evaluated the HMM algorithm using actigraphy and PSG data from 43 participants in the Multi-Ethnic Study of Atherosclerosis.
  • Compared epoch-by-epoch and sleep variable estimates against Actiwatch Software (AS) and UCSD algorithms using PSG as the ground truth.

    • Main Results:

    • The HMM algorithm achieved an accuracy of 85.7%, comparable to AS (84.7%) and UCSD (85.0%), but with significantly higher specificity (36.4% vs. 30.0% and 31.7%).
    • HMM demonstrated superior performance in Bland-Altman analysis for total sleep time, sleep latency, and sleep efficiency, showing closer agreement with PSG and narrower limits of agreement.
    • The HMM approach successfully differentiated active and sedentary individuals based on activity count variability, offering insights into sedentary behavior patterns.

    Conclusions:

    • The proposed unsupervised HMM algorithm offers a robust and individualized method for sleep/wake identification from actigraphy, outperforming current software and supervised methods in key metrics.
    • HMM enhances the utility of actigraphy in research settings where PSG is impractical or supervised training data is absent.
    • The algorithm's ability to characterize individual activity patterns provides a valuable tool for downstream analyses of behavior and health.