Cyclostationary approach to Doppler radar heart and respiration rates monitoring with body motion cancelation using Radar Doppler System

Kazemi, Somayeh; Ghorbani, Ayaz; Amindavar, Hamidreza; Li, Changzhi

doi:10.1016/j.bspc.2014.03.012

Cited by 45 publications

(34 citation statements)

References 29 publications

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“…Using [24], it is proved that the limit periodic autocorrelation function of the signal y(t) in (17) (at the bottom of this page) will have lines in its spectral analysis, and therefore it is second-order cyclostationary with frequencies f h and f r . The cycle frequencies {α n,m } of y(t), which are of the conjugate type and of order 2, are equal to the second harmonics of all the multiple carrier frequencies: α n,m = 2nf h + 2mf r .…”

Section: Wwwietdlorgmentioning

confidence: 99%

Cyclostationary modelling of amplitude and frequency modulated signals in heart and respiration monitoring Doppler radar systems

Kazemi

Ghorbani

Amindavar

2015

IET Radar, Sonar & Navigation

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Unobtrusive and continuous monitoring of cardiopulmonary activity at a distance provides a potential tool in making health care and emergency delivery more efficient. Doppler radar remote sensing of vital signs has shown promise to this end, with proof of concept demonstrated for various applications such as more comfortable monitoring for many people. In this study, the authors present a second-order cyclostationary model for the returned amplitude and frequency modulated radar signal, which is contaminated with various noises, body motion artefacts and dc offset, to extract vital signs. They validate their model with confidence interval extraction from 500 simulation runs and also with 50 experiment runs on a female student using a miniaturised non-contact heartbeat and respiration monitoring radar system based on the quadrature Doppler effect.

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Section: Wwwietdlorgmentioning

confidence: 99%

Cyclostationary modelling of amplitude and frequency modulated signals in heart and respiration monitoring Doppler radar systems

Kazemi

Ghorbani

Amindavar

2015

IET Radar, Sonar & Navigation

Self Cite

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“…In [129], the empirical mode decomposition (EMD) signal-processing technique was used to remove motion artifacts from the antenna and the subject. Another signal-processing method was used in [130] to remove the RBM effect on human vital signs. Even though the forward and backward movements are easily cancelled out in these papers, they usually require more complex and power-consuming systems.…”

Section: Challengesmentioning

confidence: 99%

Human Vital Signs Detection Methods and Potential Using Radars: A Review

Kebe

Gadhafi

Mohammad

et al. 2020

Sensors

126

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Continuous monitoring of vital signs, such as respiration and heartbeat, plays a crucial role in early detection and even prediction of conditions that may affect the wellbeing of the patient. Sensing vital signs can be categorized into: contact-based techniques and contactless based techniques. Conventional clinical methods of detecting these vital signs require the use of contact sensors, which may not be practical for long duration monitoring and less convenient for repeatable measurements. On the other hand, wireless vital signs detection using radars has the distinct advantage of not requiring the attachment of electrodes to the subject’s body and hence not constraining the movement of the person and eliminating the possibility of skin irritation. In addition, it removes the need for wires and limitation of access to patients, especially for children and the elderly. This paper presents a thorough review on the traditional methods of monitoring cardio-pulmonary rates as well as the potential of replacing these systems with radar-based techniques. The paper also highlights the challenges that radar-based vital signs monitoring methods need to overcome to gain acceptance in the healthcare field. A proof-of-concept of a radar-based vital sign detection system is presented together with promising measurement results.

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“…Recent applications are in the following subjects: analysis of genome signals [19], neuroscience [20], ballistocardiogram analysis [128], heart and lung sound separation [129], analysis of the embolic blood Doppler signal [132], foetal PQRST extraction from electrocardiogram (ECG) recordings [143], heart and respiration rates monitoring [183], heart sound signal selection [217], heart sound cancelation from lung sound [218], detection and characterization of a runner's fatigue [38,235], analysis of electromyographic signals [296], modeling and analysis of ground reaction force signals [97,300], and analysis of myoelectric signals [3].…”

Section: Biological Signalsmentioning

confidence: 99%