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Updated: Jul 10, 2026
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Quantifying Infra-slow Dynamics of Spectral Power and Heart Rate in Sleeping Mice
Published on: August 2, 2017
Signal processing and feature extraction for sleep evaluation in wearable devices
Anna M Bianchi1, Omar P Villantieri, Martin O Mendez
1Dept. of Biomed. Eng., Polytech. Univ., Milan, Italy. annamaria.bianchi@polimi.it
Summary
Heart rate variability and respiratory signals can evaluate sleep quality, especially for wearable devices. Analysis of these signals reveals sleep patterns and microarousals.
Area of Science:
- Biomedical Engineering
- Physiology
- Sleep Science
Background:
- Wearable devices offer opportunities for continuous physiological monitoring.
- Assessing sleep quality non-invasively is crucial for health management.
- Heart rate variability (HRV) and respiration are key physiological indicators.
Purpose of the Study:
- To investigate the feasibility of sleep evaluation using HRV and respiratory signals.
- To explore the utility of frequency domain analysis for sleep assessment.
- To identify potential markers for sleep disturbances like microarousals.
Main Methods:
- Analysis of heart rate variability (HRV) and respiratory signals.
- Frequency domain analysis of spectral and cross-spectral parameters.
- Identification of microarousals based on characteristic HRV signal modifications.
Main Results:
- Spectral and cross-spectral parameters of HRV and respiration provide a basis for sleep evaluation.
- Typical modifications in the HRV signal correlate with recognized microarousals.
- The analysis demonstrates the potential for objective sleep assessment using these physiological signals.
Conclusions:
- Sleep evaluation is possible using heart rate variability and respiratory signals.
- Frequency domain analysis offers valuable insights into sleep patterns and disturbances.
- This approach holds promise for non-invasive sleep monitoring in wearable applications.
