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Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research
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Author Spotlight: IntelliSleepScorer — A High-Accuracy, Accessible GUI Software for Automated Sleep Stage Scoring in Mice and its Application in Psychiatric Research

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Deep learning for automated sleep staging using instantaneous heart rate.

Niranjan Sridhar1, Ali Shoeb1, Philip Stephens1

  • 1Verily Life Sciences, Mountain View, CA USA.

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This study introduces a deep learning algorithm for automated sleep stage scoring using heart rate data. The AI model accurately classifies sleep stages, offering a cost-effective alternative for sleep research.

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Area of Science:

  • Cardiology
  • Sleep Medicine
  • Artificial Intelligence

Background:

  • Current clinical sleep evaluations are resource-intensive and not suitable for long-term studies.
  • Sleep staging via cardiac rhythm offers a more accessible measurement method using various devices.
  • Automated sleep scoring is crucial for advancing sleep research and clinical applications.

Purpose of the Study:

  • To develop and validate a deep learning algorithm for automated sleep stage scoring using instantaneous heart rate (IHR) derived from electrocardiogram (ECG) data.
  • To assess the algorithm's performance and generalizability across different datasets and sleep scoring standards.
  • To demonstrate the utility of AI-driven sleep staging in reproducing established clinical findings.

Main Methods:

  • Applied deep learning techniques to IHR time series extracted from ECG.
  • Trained and validated the algorithm on over 10,000 nights of data from the Sleep Heart Health Study (SHHS) and Multi-Ethnic Study of Atherosclerosis (MESA).
  • Evaluated algorithm performance using accuracy and kappa statistics against reference sleep stages and tested generalization on an independent dataset.

Main Results:

  • Achieved 0.77 accuracy and 0.66 kappa for classifying sleep into wake, light, deep, and REM stages on a held-out SHHS dataset.
  • Demonstrated strong generalization to an independent dataset of 993 subjects validated by clinical staff.
  • Successfully reproduced previous clinical correlations between sleep stages and comorbidities (sleep apnea, hypertension) and demographics (age, gender).

Conclusions:

  • The developed deep learning algorithm provides accurate and generalizable automated sleep stage scoring from ECG-derived IHR.
  • This AI-driven approach offers a scalable and cost-effective solution for sleep evaluations, facilitating longer-term studies.
  • The algorithm's ability to replicate clinical findings supports its potential for use in sleep research and clinical practice.