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This study introduces a novel radar method using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to accurately measure respiration and heartbeat rates. The frequency modulated continues wave (FMCW) radar system effectively separates vital signs even for multiple individuals.

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Area of Science:

Signal Processing
Biomedical Engineering
Radar Technology

Background:

Accurate measurement of vital signs like respiration and heartbeat rates is crucial for health monitoring.
Traditional methods may have limitations in non-contact, continuous monitoring scenarios.
Frequency Modulated Continues Wave (FMCW) radar offers potential for remote physiological monitoring.

Purpose of the Study:

To develop and validate a blind source separation-based signal processing method for measuring respiration and heartbeat rates.
To enhance signal quality using advanced noise reduction techniques for improved accuracy.
To assess the method's efficacy in separating vital signs for single and multiple individuals.

Main Methods:

Utilized Frequency Modulated Continues Wave (FMCW) radar operating in the 77GHz-81GHz range.
Employed Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal preprocessing.
Decomposed phase signals into intrinsic mode functions (IMFs) and reconstructed the signal using selected IMFs.

Main Results:

Achieved accurate separation of respiration and heartbeat signals for a single person with average deviations of 1.1 bpm and 6.8 bpm, respectively.
Successfully demonstrated the separation of respiratory signals for two distinct individuals.
The CEEMDAN method significantly improved the quality and separability of the radar-detected vital signs.

Conclusions:

The proposed blind source separation method combined with CEEMDAN and FMCW radar provides a robust and accurate approach for non-contact vital sign monitoring.
This technique shows promise for applications requiring the simultaneous measurement of respiration and heartbeat rates, even in complex scenarios with multiple subjects.
Future work could explore further optimization and real-world clinical validation of this radar-based system.