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An unbiased, efficient sleep-wake detection algorithm for a population with sleep disorders: change point decoder.

Ayse S Cakmak1, Giulia Da Poian2, Adam Willats3

  • 1School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA, USA.

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Summary
This summary is machine-generated.

A new data-driven method using wrist-worn wearables accurately detects sleep-wake states in elderly men with sleep disorders, outperforming the traditional Oakley algorithm.

Keywords:
actigraphychange point detectionheart ratesleep/wakewearable device

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Area of Science:

  • Biomedical Engineering
  • Sleep Science
  • Wearable Technology

Background:

  • Detecting sleep-wake states with wrist-worn wearables is challenging, especially for individuals with sleep disorders.
  • Existing algorithms like the Oakley algorithm (OA) have limitations in accuracy for disordered sleep populations.

Purpose of the Study:

  • To develop and validate a novel, unbiased, data-driven method for sleep-wake detection using wearable sensors.
  • To compare the performance of this new method against the established Oakley algorithm using polysomnography (PSG) as a gold standard.

Main Methods:

  • Simultaneous collection of overnight polysomnography (PSG), accelerometry, and photoplethysmography from a wearable device (Empatica E4) in 102 elderly men.
  • Development of a change point decoder (CPD) algorithm that analyzes signal changes to estimate sleep-wake states.
  • Comparative analysis of CPD and OA performance against PSG.

Main Results:

  • The CPD achieved a wake accuracy of 0.74 and an Area Under the Curve (AUC) of 0.78, outperforming OA's wake accuracy (0.54) and AUC (0.67).
  • While OA had better sleep accuracy (0.85 vs. 0.70 for CPD), CPD demonstrated more balanced performance across sleep and wake states.
  • CPD showed lower errors in sleep onset latency and sleep efficiency compared to OA, despite underestimating sleep-wake transitions.

Conclusions:

  • The developed change point decoder (CPD) offers a promising alternative framework for sleep-wake state detection using wearable technology.
  • CPD's ability to integrate cardiac and motion signals provides balanced performance and higher AUC, suitable for investigating sleep dynamics.
  • This data-driven approach can be applied within standard 30-second epoch time frames for sleep research.