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Multi-Modal Home Sleep Monitoring in Older Adults
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Non-Contact Breathing Monitoring Using Sleep Breathing Detection Algorithm (SBDA) Based on UWB Radar Sensors.

Muhammad Husaini1,2, Latifah Munirah Kamarudin1,2, Ammar Zakaria1,3

  • 1Faculty of Electronic Engineering Technology, Universiti Malaysia Perlis (UniMAP), Arau 02600, Perlis, Malaysia.

Sensors (Basel, Switzerland)
|July 27, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a Sleep Breathing Detection Algorithm (SBDA) for ultra-wideband radar monitoring. SBDA accurately extracts breathing signals from non-stationary subjects, achieving a low 6.12% error rate.

Keywords:
breathing rate (BR)contactless sensingpolysomnography (PSG)sleeping monitoringultra-wideband (UWB) radar

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Area of Science:

  • Biomedical Engineering
  • Signal Processing

Background:

  • Monitoring sleep breathing is challenging for non-stationary subjects due to signal clutter and body movement interference.
  • Existing methods struggle with accurate breathing signal extraction in real-world sleep scenarios.

Purpose of the Study:

  • To propose and validate a novel Sleep Breathing Detection Algorithm (SBDA) for ultra-wideband (UWB) radar-based sleep monitoring.
  • To improve the accuracy of breathing signal extraction from non-stationary subjects.

Main Methods:

  • SBDA combines variance features with Discrete Wavelet Transform (DWT) for clutter signal removal.
  • A curve-fit sinusoidal pattern algorithm with R-square comparison differentiates chest and body movements.
  • Ensemble Empirical Mode Decomposition (EEMD) and Fast Fourier Transform (FFT) extract breathing signals and rate.

Main Results:

  • The SBDA demonstrated effective sleep breathing monitoring using IR-UWB radar.
  • Achieved the lowest average percentage error (6.12%) compared to two existing methods.
  • Validated on 15 subjects with normal and abnormal sleep monitoring ratings.

Conclusions:

  • SBDA offers a robust solution for accurate sleep breathing monitoring in non-stationary individuals.
  • The algorithm effectively addresses challenges of signal clutter and body movement interference.
  • SBDA shows significant potential for improving sleep disorder diagnosis and management.