Spatial Blind Source Estimation of Respiratory Rate and Heart Rate Detection Based on Frequency-Modulated Continuous...

Area of Science:

• Biomedical Engineering
• Signal Processing
• Radar Systems

Background:

• FMCW radar systems face challenges in accurately detecting respiratory and heart rates due to static clutter and white Gaussian noise.
• Hardware heat loss contributes to environmental noise, degrading the separation of vital sign signals.
• Harmonic components of respiratory signals can interfere with accurate heart rate estimation.

Purpose of the Study:

• To propose a spatial blind source estimation method for accurate respiratory and heart rate detection using FMCW radar.
• To address the limitations of existing methods in handling noise and signal harmonic interference.
• To improve the stability and accuracy of vital sign monitoring in cluttered environments.

Main Methods:

• Feature extraction from the intermediate frequency (IF) signal using weighted principal component analysis (WPCA).
• Reconstruction of respiratory and heart rate signals based on extracted distinct features.
• Application of the multi-signal classification (MUSIC) algorithm to transform signals into the zero domain, mitigating harmonic interference.

Main Results:

• Achieved a respiratory rate detection accuracy of 94.51%.
• Achieved a heart rate detection accuracy of 97.79%.
• Demonstrated superior stability and higher detection accuracy compared to traditional algorithms.

Conclusions:

• The proposed spatial blind source estimation method effectively enhances the accuracy of respiratory and heart rate detection via FMCW radar.
• The method successfully mitigates the impact of environmental noise and signal harmonics.
• This technique offers a stable and accurate solution for vital sign monitoring in challenging conditions.