Mathematical modeling of blood flow through an eccentric catheterized artery: A practical approach for a complex system

Ahrabi, Sima Sarv; Shojafar, Mohammad; Esfeh, H. Kazemi; Abraham, Ajith

doi:10.1109/his.2014.7086168

Cited by 5 publications

(3 citation statements)

References 13 publications

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“…The pulse wave signal and ECG signal are combined to measure the pulse wave conduction time. The pulse wave signal includes a number of harmonic syntheses and is highly impacted by the surrounding environment [26] . However, the ECG signal acquisition device is a flexible electrode, which adheres tightly to the human body and is easy to relocate.…”

Section: Blood Pressure Diagnosis Algorithmmentioning

confidence: 99%

Design of a Continuous Blood Pressure Measurement System Based on Pulse Wave and ECG Signals

Chen

et al. 2018

IEEE J. Transl. Eng. Health Med.

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With increasingly fierce competition for jobs, the pressures on people have risen in recent years, leading to lifestyle and diet disorders that result in significantly higher risks of cardiovascular disease. Hypertension is one of the common chronic cardiovascular diseases; however, mainstream blood pressure measurement devices are relatively heavy. When multiple measurements are required, the user experience and the measurement results may be unsatisfactory. In this paper, we describe the design of a signal collection module that collects pulse waves and electrocardiograph (ECG) signals. The collected signals are input into a signal processing module to filter the noise and amplify the useful physiological signals. Then, we use a wavelet transform to eliminate baseline drift noise and detect the feature points of the pulse waves and ECG signals. We propose the concept of detecting the wave shape associated with an instance, an approach that minimizes the impact of atypical pulse waves on blood pressure measurements. Finally, we propose an improved method for measuring blood pressure based on pulse wave velocity that improves the accuracy of blood pressure measurements by 58%. Moreover, the results meet the american medical instrument promotion association standards, which demonstrate the feasibility of our measurement system.

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Section: Blood Pressure Diagnosis Algorithmmentioning

confidence: 99%

Design of a Continuous Blood Pressure Measurement System Based on Pulse Wave and ECG Signals

Chen

et al. 2018

IEEE J. Transl. Eng. Health Med.

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“…[1][2][3][4] The beat-to-beat BP data reveal a great deal of information about the dynamic features of cardiovascular control mechanisms, 5 which is significant for both disease prevention and clinical diagnosis. [6][7][8] At present, most of the clinical approaches are cuff based, which is uncomfortable during the inflation process. Therefore, cuff-less technique is much more desirable.…”

Section: Introductionmentioning

confidence: 99%

Noninvasive blood pressure estimation with peak delay of different pulse waves

Song

et al. 2019

International Journal of Distributed Sensor Networks

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In this article, we discuss the validity of noninvasive continuous blood pressure estimation with two different types of peripheral pulse waves. Artery-blocking experiment shows that phase difference of two pulse waves at the same location is well related with blood pressure and blood flow fluctuation. Exercise-recovery experiment resulting from 16 subjects shows that phase difference varies with blood pressure with the correlation from 0.63 to 0.88 when blood pressure changes rapidly. Simulations based on a classic hemodynamic model verify the relationship between phase difference and blood pressure. However, phase difference is strongly correlated with smooth muscle state of the arterial wall as well. If smooth muscle information can be obtained by further study, phase difference can act as a promising approach to portable and wearable device for real-time blood pressure monitoring.

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“…With the rapid development of electronics technology miniaturization [1], the monitoring of human health is gaining more and more attention. One public health report in 2010 showed that almost 1 in every 4 deaths every year in the United States is due to heart disease [2].…”

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confidence: 99%

Spectral Matrix Decomposition-Based Motion Artifacts Removal in Multi-Channel PPG Sensor Signals

et al. 2016

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The intelligent wearable heart rate measurement requirement has attracted more and more attention, and the related applications of Internet of Things are emerging. However, under intensive physical exercises, motion artifacts are strong interference sources for wrist-type photoplethysmography (PPG) sensor signals, thus significantly affecting the accurate estimation of heart rate and other physiological parameters. Currently, how to effectively remove the motion artifacts from PPG sensor signals is becoming an active and challenging research realm. In this paper, we propose a multi-channel spectral matrix decomposition (MC-SMD) model to accurately estimate heart rate in the presence of intensive physical activities. Motivated by the observation that the PPG signal spectrum and the acceleration spectrum have almost the same spectral peak positions in the frequency domain, we first model the removal of motion artifacts as a spectral matrix decomposition optimization problem. After removing motion artifacts, we propose a new spectral peak tracking method for estimating heart rate. Experimental results on the wellknown PPG data sets recorded from 12 subjects during intensive movements demonstrate that MC-SMD can efficiently remove the motion artifacts and retrieve an accurate heart rate using multi-channel PPG sensor signals.INDEX TERMS Accelerated proximal gradient (APG) method, compressive sensing, heart rate measurement, photoplethysmography (PPG). I. INTRODUCTION A. BACKGROUND AND MOTIVATION

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Mathematical modeling of blood flow through an eccentric catheterized artery: A practical approach for a complex system

Cited by 5 publications

References 13 publications

Design of a Continuous Blood Pressure Measurement System Based on Pulse Wave and ECG Signals

Design of a Continuous Blood Pressure Measurement System Based on Pulse Wave and ECG Signals

Noninvasive blood pressure estimation with peak delay of different pulse waves

Spectral Matrix Decomposition-Based Motion Artifacts Removal in Multi-Channel PPG Sensor Signals

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