A real-time heart rate analysis for a remote millimeter wave I-Q sensor

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 6; pp. 1839 - 1845
• Main Authors: Bakhtiari, Sasan, Liao, Shaolin, Elmer, Thomas, "Sami" Gopalsami, Nachappa, Raptis, A. C.
• Format: Journal Article
• Language: English
• Published: New York, NY IEEE 01.06.2011; Institute of Electrical and Electronics Engineers; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithm; Algorithms; Biological and medical sciences; Biometry; Chi-Square Distribution; Computerized, statistical medical data processing and models in biomedicine; Discrete Fourier transforms; Fourier Analysis; Fourier transforms; Fundamental and applied biological sciences. Psychology; Heart beat; Heart rate; heart rate (HR); Heart Rate - physiology; Human subjects; Humans; Medical management aid. Diagnosis aid; Medical sciences; millimeter wave (mmW) radar; Monitoring, Physiologic - methods; Nonlinear Dynamics; Optimization methods; Radar; Respiratory Rate; Studies; Vertebrates: cardiovascular system; Biometrics; Human; millimeter wave (mmW) radar; Fourier transformation; Measurement sensor; Microwave; Algorithm; Heart rate; Millimetric wave; Radar; Signal processing; Real time processing; heart rate (HR); Comparative study; Biomedical engineering
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2011.2122335

This paper analyzes heart rate (HR) information from physiological tracings collected with a remote millimeter wave (mmW) I-Q sensor for biometric monitoring applications. A parameter optimization method based on the nonlinear Levenberg-Marquardt algorithm is used. The mmW sensor works at 94 GHz and can detect the vital signs of a human subject from a few to tens of meters away. The reflected mmW signal is typically affected by respiration, body movement, background noise, and electronic system noise. Processing of the mmW radar signal is, thus, necessary to obtain the true HR. The down-converted received signal in this case consists of both the real part (I-branch) and the imaginary part (Q-branch), which can be considered as the cosine and sine of the received phase of the HR signal. Instead of fitting the converted phase angle signal, the method directly fits the real and imaginary parts of the HR signal, which circumvents the need for phase unwrapping. This is particularly useful when the SNR is low. Also, the method identifies both beat-to-beat HR and individual heartbeat magnitude, which is valuable for some medical diagnosis applications. The mean HR here is compared to that obtained using the discrete Fourier transform.