Vital-Sign Extraction Using Bootstrap-Based Generalized Warblet Transform in Heart and Respiration Monitoring Radar System

• Published in: IEEE transactions on instrumentation and measurement 2016-02, Vol.65 (2), p.255-263
• Main Authors: Kazemi, Somayeh, Ghorbani, Ayaz, Amindavar, Hamidreza, Morgan, Dennis R.
• Format: Article
• Language: English
• Subjects: Bootstrap method; Digital broadcasting; Doppler radar; Frequency modulation; generalized warblet transform (GWT); nonlinear frequency modulation; nonstationary signal; Radar; Signal processing; Signal to noise ratio; Time-frequency analysis; Transforms
• ISSN: 0018-9456; 1557-9662
• DOI: 10.1109/TIM.2015.2482230

In biomedical Doppler radar applications, the return signal is a nonlinear frequency-modulation (NLFM) random process whose phase conveys heart and respiration vital-sign information. These signatures modulate the phase of the signal as two oscillating components with frequencies less than a few hertz. Due to the nonstationary nature of these signals, their analysis by 1-D techniques, temporal and spectral, may not be very useful, and time-frequency techniques may be incapable of accurately extracting their instantaneous frequency (IF) trajectory. In this paper, we present a bootstrap-based generalized warblet transform (GWT) signal processing method. The presented signal processing tool is a parametric method that has a kernel with Fourier-series components. The coefficients of the kernel are estimated by an iteration procedure that converges to the IF of the radar signal. We show theoretically and experimentally that the bootstrap-based GWT can extract the amplitude and frequency of the two vital-sign components at a range of 3 m in the face of low signal-to-noise ratio and in the presence of phase noise and body motion artifacts, achieving an accuracy that is potentially better than conventional methods can provide.