Heart Rate Variability Assessment Using Doppler Radar with Linear Demodulation

Borić–Lubecke, Olga; Massagram, Wansuree; Lubecke, Victor M.; Høst-Madsen, A.; Jokanovic, Branka

doi:10.1109/eumc.2008.4751478

Cited by 28 publications

(26 citation statements)

References 14 publications

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“…Currently, the first commercial respiration monitors are entering the market [3], [4]. However, a number of challenges still remain with radar monitoring, including development of nonlinear channel combining algorithms [5]- [7], removal of motion and respiration artifacts of the patient [8], [9] and the background, and development of rate detection methods for heart rate variability (HRV) analysis [10]- [13]. Rate detection should be robust for the changes in the signal waveform that occur due to the measurement position.…”

Section: Introductionmentioning

confidence: 99%

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

2012

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Microwave doppler radar offers significant improvements for unobtrusive heart and respiration measurement. Radar monitoring enables non-contact measurement, through clothing, of heart and respiration rate, which is desired in several applications ranging from medical sleep laboratory measurements to home health care measurements and stress monitoring. The use of high frequency radar ( 10 GHz) instead of lower frequencies ( 2.4 GHz) increases the signal-to-noise-ratio of the signal and enables the utilization of commercial radar modules. However, if high frequency radar is used, linear combining of quadrature radar channels is inadequate. Instead, a nonlinear channel combining algorithm is needed. The combining can be performed with an arctangent function if center, amplitude error, and phase error are estimated accurately and corrected. In this paper, we show that the Levenberg-Marquardt (LM) center estimation algorithm outperforms the state-of-the-art center estimation algorithm precision-wise and is computationally less complex. The simulated results show that the root mean squared error with the LM method is always less than 1%, while it is around 5%-13% with the compared method, depending on the breathing signal model used. In addition, the computational complexity of the LM method stays almost constant as the size of the data set increases, whereas with the reference method, it increases exponentially. In this paper, the LM method is validated both with simulations and with real data.Index Terms-Biomedical signal processing, Doppler radar measurement, non-contact heart and respiration measurement, physiological monitoring, remote sensing.

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Section: Introductionmentioning

confidence: 99%

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

2012

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“…In [7], error in beat-to-beat intervals (root-mean-square of differences of successive beatto-beat intervals (RMSSD)) measured by Doppler radar and reference ECG differed less than ±76 ms. In [8], root-meansquare error of intervals obtained by Doppler radar was 56 ms, compared to finger pulse reference. These errors can be considered rather large compared to the accuracy attained by ECG, which is in the order of milliseconds.…”

Section: Resultsmentioning

confidence: 99%

“…In addition, there are applications where obtrusive contact electrodes could disturb the measurement itself such as sleep quality monitoring in a medical sleep laboratory. The current challenges with radar monitoring, however, include removal of motion and respiration artifacts of the patient [1,2] and the background, development of a non-linear channel combining algorithm [3,4], estimation of amplitude and phase imbalance of the two radar channels [5], and development of rate detection methods [6][7][8]. Rate detection should be robust for the changes in the signal waveform due to the measurement position.…”

Section: Introductionmentioning

confidence: 99%

Separating respiration artifact in microwave doppler radar heart monitoring by Independent Component Analysis

Zakrzewski

Vanhala

2010

2010 IEEE Sensors

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With a microwave radar, chest wall movements originating from cardiac and respiratory activity can be recorded non-contactly. A major challenge is how to separate the desired low-amplitude cardiac signal from large-amplitude artifacts, such as respiration. Commonly, the separation is performed with a narrow band-pass filter. This causes the signal edges to be rounded, which complicates the accurate timing estimation in the heart rate variability (HRV) analysis. In addition, the harmonics of the respiration signal might fall into the same frequency spectrum as the cardiac signal. In this study, we recorded data with two radars and studied signal separation using a complex-valued Independent Component Analysis (ICA). After ICA, the respiratory signal is greatly attenuated, although still present, in two of the independent components (ICs). However, respiration harmonics are reduced as well, and thus, the residual respiratory signal can be removed by filtering.

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“…6,15,19,23,33 In contrast to this past work, which recovers the average period of a heartbeat (which is of the order of a second), emotion recognition requires extracting the individual heartbeats and measuring small variations in the beat-to-beat intervals with millisecondscale accuracy. Unfortunately, prior research that aims to segment RF reflections into individual beats either cannot achieve sufficient accuracy for emotion recognition 11,20,31 or requires the monitored subjects to hold their breath. 41 In particular, past work that does not require users to hold their breath has an average error of 30-50ms, 11,20, 31 whereas EQ-Radio achieves average accuracy of 3.2ms.…”

Section: Beat Extraction Algorithmmentioning

confidence: 99%

“…Unfortunately, prior research that aims to segment RF reflections into individual beats either cannot achieve sufficient accuracy for emotion recognition 11,20,31 or requires the monitored subjects to hold their breath. 41 In particular, past work that does not require users to hold their breath has an average error of 30-50ms, 11,20, 31 whereas EQ-Radio achieves average accuracy of 3.2ms.…”

Section: Beat Extraction Algorithmmentioning

confidence: 99%

Emotion recognition using wireless signals

2018

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This paper demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-theart emotion recognition systems that require a person to be hooked to an ECG monitor.

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Heart Rate Variability Assessment Using Doppler Radar with Linear Demodulation

Cited by 28 publications

References 14 publications

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Comparison of Center Estimation Algorithms for Heart and Respiration Monitoring With Microwave Doppler Radar

Separating respiration artifact in microwave doppler radar heart monitoring by Independent Component Analysis

Emotion recognition using wireless signals

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