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Relationship between heart rate variability using Lorenz plot and sleep level.

Kosuke Tsuboi1, Akihiro Deguchi, Hiroshi Hagiwara

  • 1Graduate School of Science and Engineering, Advanced Information Science and Engineering Major, Human Information Science Course, Ritsumeikan University, 1-1-1 Noji Higashi, Kusatsu, Shiga 525-8577, Japan. ci008047@ed.ritsumei.ac.jp

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
|November 25, 2010
PubMed
Summary

This study introduces a novel method using electrocardiogram (ECG) RR intervals to estimate sleep depth. The technique analyzes heart rate variability (HRV) patterns on a Lorenz plot to accurately assess sleep stages throughout the night.

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Area of Science:

  • Cardiology
  • Sleep Medicine
  • Biomedical Engineering

Background:

  • Accurate sleep stage estimation is crucial for diagnosing sleep disorders.
  • Current polysomnography methods are resource-intensive.
  • Heart rate variability (HRV) offers a non-invasive alternative for physiological monitoring.

Purpose of the Study:

  • To develop and validate a new technique for estimating sleep depth using electrocardiogram (ECG) RR intervals (RRIs).
  • To assess the correlation between HRV patterns and sleep stages.
  • To evaluate the feasibility of using Lorenz plots for real-time sleep monitoring.

Main Methods:

  • Collected all-night ECG RRIs from participants.
  • Generated Lorenz plots (LPs) from RRIs to visualize heart rate variability.
  • Analyzed shifts in the mean (center C) and standard deviation (area S) of LP distributions across different sleep stages.
  • Compared HRV-derived sleep level estimations with actual sleep stages.

Main Results:

  • Changes in Lorenz plot distributions were observed corresponding to different sleep stages.
  • Center C shifted towards lighter sleep levels, while area S indicated transitions into deep sleep.
  • A 60.1% concordance rate was achieved between estimated and actual transitional sleep levels.

Conclusions:

  • Transitional sleep levels can be effectively evaluated using heart rate variability (HRV) analysis via Lorenz plots.
  • This non-invasive method shows promise for accessible sleep depth estimation.
  • Further research can refine this technique for clinical applications in sleep medicine.