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  1. Home
  3. Interpretable Framework For Sleep Monitoring: Applying Statistical Control Charts To Physiological Data Streams.
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  3. Interpretable Framework For Sleep Monitoring: Applying Statistical Control Charts To Physiological Data Streams.

Related Experiment Video

IntelliSleepScorer, a Software Package with a Graphic User Interface for Mice Automated Sleep Stage Scoring
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Interpretable Framework for Sleep Monitoring: Applying Statistical Control Charts to Physiological Data Streams.

Rupesh Agrawal1, Dursun Delen2,3, Bruce Benjamin4

  • 1Department of Health Informatics, College of Informatics, Northern Kentucky University, Highland Heights, KY 41099, USA.

Sensors (Basel, Switzerland)
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Statistical process control (SPC) charts offer a transparent way to analyze sleep physiology. Control chart rule violations correlate with sleep stages, aiding interpretable sleep data analysis.

Keywords:
analyticsanomaly detectionbig datacontrol chartsdecision support systempatient safetystreaming

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Area of Science:

  • Physiological monitoring
  • Statistical process control
  • Sleep science

Background:

  • Polysomnography (PSG) generates complex, non-linear physiological time-series data, posing interpretability challenges.
  • Analyzing sleep-related physiological variability requires transparent and explainable methods.

Purpose of the Study:

  • To explore the feasibility of applying statistical process control (SPC) charts to cardio-respiratory signals from PSG.
  • To assess if SPC control charts can provide transparent and interpretable analysis of sleep-related physiological variability.

Main Methods:

  • Cardio-respiratory signals from a public PSG dataset were preprocessed and analyzed using univariate control charts.
  • Sleep stage annotations were used to contextualize physiological variability across wake and non-REM sleep.
  • Control chart rule violations were quantitatively examined relative to sleep-state transitions.

    • Main Results:

    • Control chart rule violations were more frequent during wakefulness and wake-non-REM sleep transitions.
    • Variability flags from SPC control charts showed structural correspondence with annotated sleep stage dynamics.
    • Control charts remained relatively stable during sustained non-REM sleep.

    Conclusions:

    • SPC control charts offer a transparent and interpretable framework for analyzing physiological variability in sleep data.
    • This method supports future research in sleep-state analysis and explainable data-driven approaches.
    • The study demonstrates feasibility, not diagnostic performance or accuracy.