Signal processing methods for non-contact cardiac detection using Doppler radar

Vasu, V.; Heneghan, Conor; Arumugam, Thianantha; Sezer, Sakir

doi:10.1109/sips.2010.5624817

Cited by 10 publications

(8 citation statements)

References 7 publications

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“…Many technologies for remotely tracking the cardiorespiratory signal which contains important physiological signs that have been proposed during the last decades, include technologies based on Doppler radar [6] – [11] , thermal imaging [12] – [17] , and capacitive coupled electrocardiography (ECG) measurement [18] – [21] . However, these technologies have been reported to be able to only extract the cardiorespiratory signal from a single subject at a time, and they are highly affected by noise artifacts particularly with a moving subject.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal

Al-Naji

Chahl

2017

IEEE J. Transl. Eng. Health Med.

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Most existing non-contact monitoring systems are limited to detecting physiological signs from a single subject at a time. Still, another challenge facing these systems is that they are prone to noise artifacts resulting from motion of subjects, facial expressions, talking, skin tone, and illumination variations. This paper proposes an efficient non-contact system based on a digital camera to track the cardiorespiratory signal from a number of subjects (up to six persons) at the same time with a new method for noise artifact removal. The proposed system relied on the physiological and physical effects as a result of the activity of the cardiovascular and respiratory systems, such as skin color changes and head motion. Since these effects are imperceptible to the human eye and highly affected by the noise variations, we used advanced signal and video processing techniques, including developing video magnification technique, complete ensemble empirical mode decomposition with adaptive noise, and canonical correlation analysis to extract the heart rate and respiratory rate from multiple subjects under the noise artifact assumptions. The experimental results of the proposed system had a significant correlation (Pearson’s correlation coefficient = 0.9994, Spearman correlation coefficient = 0.9987, and root mean square error = 0.32) when compared with the conventional contact methods (pulse oximeter and piezorespiratory belt), which makes the proposed system a promising candidate for novel applications.

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Section: Introductionmentioning

confidence: 99%

Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal

Al-Naji

Chahl

2017

IEEE J. Transl. Eng. Health Med.

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“…Non-contact measurements of physiological parameters can be achieved using thermal infrared imaging, a technology employed by Pavlidis et al to collect physiological data on human faces like heart and respiratory rates, perspiration, supraorbital and periorbital blood flow [4][5][6][7]. Similarly, Doppler radars are non-contact sensors that were used to detect heartbeats [8,9] and respiration signals [10]. More recently, digital cameras and webcams were employed on the face to detect Blood Volume Pulse (BVP) [11][12][13][14][15] and compute HR and Breathing Rate (BR).…”

Section: Introductionmentioning

confidence: 99%

Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate

Bousefsaf

Maaoui

Pruski

2013

Biomedical Signal Processing and Control

151

120

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Photoplethysmographic signals obtained from a webcam are analyzed through a continuous wavelet transform to assess the instantaneous heart rate. The measurements are performed on human faces. Robust image and signal processing are introduced to overcome drawbacks induced by light and motion artifacts. In addition, the respiration signal is recovered using the heart rate series by respiratory sinus arrhythmia, the natural variation in heart rate driven by the respiration. The presented algorithms are implemented on a mid-range computer and the overall method works in real-time. The performance of the proposed heart and breathing rates assessment method was evaluated using approved contact probes on a set of 12 healthy subjects. Results show high degrees of correlation between physiological measurements even in the presence of motion. This paper provides a motion-tolerant method that remotely measures the instantaneous heart and breathing rates. These parameters are particularly used in telemedicine and affective computing, where the heart rate variability analysis can provide an index of the autonomic nervous system.

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“…Non-contact measurements of physiological parameters can be achieved using thermal infrared imaging, a technology employed by Pavlidis et al to collect physiological data on human faces [4]. Similarly, Doppler radars are non-contact sensors that were used to detect heartbeats [5] and respiration signals [6]. More recently, digital cameras and webcams were employed on the face to detect the blood volume pulse [7] and compute HR and breathing rate.…”

Section: Introductionmentioning

confidence: 99%

Remote assessment of the Heart Rate Variability to detect mental stress

Bousefsaf¹,

Maaoui²,

Pruski³

2013

Proceedings of the ICTs for Improving Patients Rehabilitation Research Techniques

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In the present paper, we introduce a new framework for detecting workload changes using video frames obtained from a low-cost webcam. The measurements are performed on human faces and the proposed algorithms were developed to be motion-tolerant. An interactive Stroop color word test is employed to induce stress on a set of twelve participants. We record the skin conductance and compare these responses to the stress curve assessed by a webcam-derived heart rate variability analysis. The results offer further support for the applicability of stress detection by remote and low-cost means, providing an alternative to conventional contact techniques.

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Signal processing methods for non-contact cardiac detection using Doppler radar

Cited by 10 publications

References 7 publications

Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal

Simultaneous Tracking of Cardiorespiratory Signals for Multiple Persons Using a Machine Vision System With Noise Artifact Removal

Continuous wavelet filtering on webcam photoplethysmographic signals to remotely assess the instantaneous heart rate

Remote assessment of the Heart Rate Variability to detect mental stress

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