Detecting changes in the human heartbeat with on-body radar

Aardal, Øyvind; Brovoll, Sverre; Paichard, Yoann; Berger, Tor; Lande, Tor Sverre; Hamran, Svein‐Erik

doi:10.1109/radar.2013.6586027

Cited by 19 publications

(13 citation statements)

References 15 publications

(14 reference statements)

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“…Although the sternum is attractive for antenna placement, given its relative flatness and small lung volume at that location , this investigation indicates that other locations on the chest should be investigated for producing a signal feature that is uniform in shape across different persons . Using an on-body antenna at 1GHz with the heart / aorta approximately 2-5cm away [58] means that these target organs are within in the antenna's near field. Given an inhomogenous and uneven contour of the reflection surfaces within the body, the reflection from the target and clutter will vary depending on the antenna's location and blood pressure.…”

Section: Discussionmentioning

confidence: 99%

“…The dominating source of phase modulations has been proven to be the motion at the surface of the body [27]. When the radar antennas have been placed in contact with the body, the radar phase modulations have been hypothesized to be due to propagation path loss [28] within the body, body surface motions [29], [30], or changes in near-field coupling due to body tissue movement [24], [31], [32], or far field motions of the heart [33].…”

Section: A Overviewmentioning

confidence: 99%

“…Furthermore, in order to direct the radiation towards the heart, the antenna PCBs were embedded into three stacked strips of the same RF absorbing material of 10cm length and 5 cm width. By embedding the antennas in the absorbing material, most of the energy was transmitted either into the chest or absorbed in the absorbers, limiting the disturbance from other movements in the room [33]. The CW radar antennas are placed in front of the ascending aorta or the aortic arch.…”

Section: A Overviewmentioning

confidence: 99%

“…Depending on the tissue layer that the transmitted CW signal passes through, the wavelength will be 4.1-11.6cm. With the heart and ascending aorta being about 3cm away from the antenna [33], a near field scenario may be occurring. The received signal R CW (t) at the antenna will be affected by nearby reflective boundaries, resulting in RDR(t) to vary in morphology between individuals.…”

Section: A Overviewmentioning

confidence: 99%

See 3 more Smart Citations

Blood Pressure Estimation Using Pulse Transit Time From Bioimpedance and Continuous Wave Radar

Buxi

Redout

Yuce

2017

IEEE Trans. Biomed. Eng.

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Section: Discussionmentioning

confidence: 99%

Section: A Overviewmentioning

confidence: 99%

Section: A Overviewmentioning

confidence: 99%

Section: A Overviewmentioning

confidence: 99%

See 2 more Smart Citations

Blood Pressure Estimation Using Pulse Transit Time From Bioimpedance and Continuous Wave Radar

Buxi

Redout

Yuce

2017

IEEE Trans. Biomed. Eng.

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“…In the year 2009 Aardal, Ø., et.al [10] exhibited body sensor systems (BSNs) as high preparation is required in decompression that would squander profitable vitality in the asset and force obliged sensor hubs. In this paper, to analyze cardiovascular irregularity, for example, Ventricular tachycardia, a novel framework to break down and characterize compacted ECG signal by utilizing a PCA for highlight extraction and k-mean for bunching of ordinary and unusual ECG signals is proposed.…”

Section: Literature Surveymentioning

confidence: 99%

Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients

Mahboob¹,

Sahaheen²,

Tahir³

et al. 2017

ijacsa

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Abstract-The grouping of information is a typical method in Machine learning. Information mining assumes a crucial part to extract learning from vast databases from operational databases. In medicinal services Data mining is a creating field of high significance, giving expectations and a more profound comprehension of restorative information sets. Most extreme information mining technique relies on an arrangement of elements that characterizes the conduct of the learning calculation furthermore straightforwardly or by implication impact of the multifaceted nature of models. Coronary illness is the main sources of death over the past years. Numerous scientists utilize a few information digging methods for the diagnosing of coronary illness. Diabetes is one of the incessant maladies that emerge when the pancreas does not deliver enough insulin. The vast majority of the frameworks have effectively utilized Machine learning strategies, for example, Naïve Bayes Algorithm, Decision Trees, logistic Regression and Support Vector Machines to name a few. These techniques solely rely on grouping of the information with respect to finding the heart variations from the norm. Bolster vector machine is an advanced strategy has been effectively in the field of machine learning. Utilizing coronary illness determination, the framework presented predicts using characteristics such as, age, sex, cholesterol, circulatory strain, glucose and the odds of a diabetic patient getting a coronary illness using machine learning algorithms.

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Radar-Based Heart Sound Detection

Will

Shi

Schellenberger

et al. 2018

Sci Rep

124

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This paper introduces heart sound detection by radar systems, which enables touch-free and continuous monitoring of heart sounds. The proposed measurement principle entails two enhancements in modern vital sign monitoring. First, common touch-based auscultation with a phonocardiograph can be simplified by using biomedical radar systems. Second, detecting heart sounds offers a further feasibility in radar-based heartbeat monitoring. To analyse the performance of the proposed measurement principle, 9930 seconds of eleven persons-under-tests’ vital signs were acquired and stored in a database using multiple, synchronised sensors: a continuous wave radar system, a phonocardiograph (PCG), an electrocardiograph (ECG), and a temperature-based respiration sensor. A hidden semi-Markov model is utilised to detect the heart sounds in the phonocardiograph and radar data and additionally, an advanced template matching (ATM) algorithm is used for state-of-the-art radar-based heartbeat detection. The feasibility of the proposed measurement principle is shown by a morphology analysis between the data acquired by radar and PCG for the dominant heart sounds S1 and S2: The correlation is 82.97 ± 11.15% for 5274 used occurrences of S1 and 80.72 ± 12.16% for 5277 used occurrences of S2. The performance of the proposed detection method is evaluated by comparing the F-scores for radar and PCG-based heart sound detection with ECG as reference: Achieving an F1 value of 92.22 ± 2.07%, the radar system approximates the score of 94.15 ± 1.61% for the PCG. The accuracy regarding the detection timing of heartbeat occurrences is analysed by means of the root-mean-square error: In comparison to the ATM algorithm (144.9 ms) and the PCG-based variant (59.4 ms), the proposed method has the lowest error value (44.2 ms). Based on these results, utilising the detected heart sounds considerably improves radar-based heartbeat monitoring, while the achieved performance is also competitive to phonocardiography.

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Detecting changes in the human heartbeat with on-body radar

Cited by 19 publications

References 15 publications

Blood Pressure Estimation Using Pulse Transit Time From Bioimpedance and Continuous Wave Radar

Blood Pressure Estimation Using Pulse Transit Time From Bioimpedance and Continuous Wave Radar

Evaluating Predictive Algorithms using Receiver-Operative Characteristics for Coronary Illness among Diabetic Patients

Radar-Based Heart Sound Detection

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