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Related Concept Videos

Sleep-Wake Cycles01:24

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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).
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Stages of Sleep01:22

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Sleep progresses through distinct stages, each characterized by specific brain wave patterns and physiological responses ranging from wakefulness to stages of non-rapid eye movement, known as non-REM, to rapid eye movement, referred to as REM. Understanding these stages helps in recognizing how sleep supports various bodily and cognitive functions.
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Updated: May 20, 2025

Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
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Data-driven sleep structure deciphering based on cardiorespiratory signals.

Ming Huang1, Osuke Iwata2, Kiyoko Yokoyama3

  • 1Institute of Advanced Computing and Digital Engineering, Shenzhen Institute of Advanced Technology, Shenzhen, China; Graduate School of Science and Technology, Nara Institute of Science and Technology, Ikoma, Japan.

Computer Methods and Programs in Biomedicine
|May 1, 2025
PubMed
Summary
This summary is machine-generated.

Cardiorespiratory signals can accurately identify sleep stages like wake, deep sleep, and REM. This novel approach offers a practical, EEG-independent method for sleep analysis, especially in home settings.

Keywords:
Cardiorespiratory signalsDeep-learning modelSleep apneaSleep stagesSleep structure

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Area of Science:

  • Physiological signal processing
  • Sleep science
  • Machine learning in healthcare

Background:

  • Cardiopulmonary coupling (CPC) offers a novel perspective on sleep structure analysis.
  • Current sleep staging relies on electroencephalogram (EEG) and electrooculogram (EOG) data, lacking tailored labels for cardiorespiratory signals.
  • Optimal analysis of cardiorespiratory signals for sleep structure requires segments of 4-8 minutes.

Purpose of the Study:

  • To adapt American Academy of Sleep Medicine (AASM) sleep stage labels for cardiorespiratory signal analysis.
  • To develop and evaluate a physiologically-inspired deep-learning model (PIDM) for sleep stage recognition using cardiorespiratory data.
  • To assess the feasibility of using cardiorespiratory signals for accurate, EEG-independent sleep structure recognition.

Main Methods:

  • Modified AASM labels by excluding the ambiguous N2 stage, focusing on wake, N1, deep sleep (N3), and rapid eye movement (REM) stages.
  • Developed a physiologically-inspired deep-learning model (PIDM) to extract features from cardiorespiratory time series.
  • Assessed the physiological validity of N2 predictions using the high-frequency (HF)-to-low-frequency (LF) ratio and respiratory variability.

Main Results:

  • The PIDM model achieved high balanced accuracy scores across sleep stages in normal and sleep apnea groups (e.g., 0.95 for deep sleep in sleep apnea groups).
  • Post-analysis confirmed that most classified N2 samples corresponded to stable non-rapid eye movement (NREM) sleep.
  • Physiological markers (HF-to-LF ratio, respiratory variability) aligned with established understanding of sleep stages.

Conclusions:

  • Cardiorespiratory signals are physiologically relevant for accurate sleep structure recognition.
  • Excluding and redefining the N2 stage improved the pipeline's ability to distinguish key sleep stages.
  • Cardiorespiratory signals offer a robust, practical, and EEG-independent alternative for sleep analysis, suitable for home healthcare.