Remote Sensing of Heart Rate and Patterns of Respiration on a Stationary Subject Using 94-GHz Millimeter-Wave Interferometry

• Published in: IEEE transactions on biomedical engineering Vol. 58; no. 6; pp. 1671 - 1677
• Main Authors: Mikhelson, Ilya V., Bakhtiari, Sasan, Elmer, II, Thomas W., Sahakian, Alan V.
• 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: 60 APPLIED LIFE SCIENCES; Antennas; BASIC BIOLOGICAL SCIENCES; Biological and medical sciences; Cardiography; CHEST; Computerized, statistical medical data processing and models in biomedicine; Electrocardiography; Electrocardiography. Vectocardiography; Electrodiagnosis. Electric activity recording; HEART; Heart Function Tests - instrumentation; Heart Function Tests - methods; Heart rate; Heart Rate - physiology; Humans; INTERFEROMETRY; Interferometry - instrumentation; Interferometry - methods; Investigative techniques, diagnostic techniques (general aspects); Medical management aid. Diagnosis aid; Medical sciences; millimeter wave; Millimeter wave technology; noncontact; QUADRATURES; REMOTE SENSING; RESPIRATION; Respiratory Rate - physiology; Signal generators; Signal Processing, Computer-Assisted; Skin; WAVE FORMS; Wavelet Analysis; Human; noncontact; Microwave; Remote sensing; Heart rate; Electrodiagnosis; Millimetric wave; millimeter wave; Wavelet transformation; Signal processing; Electrocardiography; Respiratory frequency; Respiration; Cardiography; Biomedical engineering; Interferometry; Non contact measurement
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2011.2111371

Using continuous wave, 94-GHz millimeter-wave interferometry, a signal representing chest wall motion can be obtained that contains both the heart rate and respiration patterns of a human subject. These components have to be separated from each other in the received signal. Our method was to use the quadrature and in-phase components of the signal, after removing the mean of each, to find the phase, unwrap it, and convert it to a displacement measurement. Using this, the power spectrum was examined for peaks, which corresponded to the heart rate and respiration rate. The displacement waveform of the chest was also analyzed for discrete heartbeats using a novel wavelet decomposition technique.