A real-time heart rate analysis for a remote millimeter wave I-Q sensor
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...
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| Published in | IEEE transactions on biomedical engineering Vol. 58; no. 6; pp. 1839 - 1845 |
|---|---|
| Main Authors | , , , , |
| 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 | |
| Online Access | Get full text |
| ISSN | 0018-9294 1558-2531 1558-2531 |
| DOI | 10.1109/TBME.2011.2122335 |
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| Abstract | 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. |
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| AbstractList | 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. 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.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. |
| Author | Raptis, A. C. Bakhtiari, Sasan "Sami" Gopalsami, Nachappa Liao, Shaolin Elmer, Thomas |
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| Cites_doi | 10.4015/S101623720500038X 10.1109/TBME.1985.325532 10.1016/j.measurement.2009.01.003 10.1109/TBME.2003.808805 10.1016/0038-1101(90)90176-F 10.1109/10.43620 10.1109/51.664031 10.1109/TMTT.2008.2007139 10.1109/58.677607 10.1007/11866763_13 10.4015/S1016237202000371 10.1161/01.CIR.95.1.151 10.1186/1475-925X-8-4 10.1109/78.258082 10.1088/0957-0233/15/12/N01 10.1137/0111030 10.1109/APMC.2007.4554533 10.1109/10.83591 10.1088/0031-9155/50/9/N02 10.1007/BF02443287 10.1063/1.2798937 10.1063/1.3114155 10.1109/IEMBS.1997.754529 10.1090/qam/10666 10.1109/CIC.2005.1588204 10.1063/1.2769353 10.1109/RADAR.2009.4977021 10.1126/science.6166045 |
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| Keywords | 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 |
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| SubjectTerms | 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 |
| Title | A real-time heart rate analysis for a remote millimeter wave I-Q sensor |
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