Assessing the blood pressure waveform of the carotid artery using an ultrasound image processing method

Soleimani, Effat; Mokhtari‐Dizaji, Manijhe; Fatouraee, Nasser; Saberi, Hooshang

doi:10.14366/usg.16019

Cited by 15 publications

(16 citation statements)

References 36 publications

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“…Ultrasound imaging is a non-invasive technique that has been used to monitor the function of arteries and veins. Ultrasound can be used to measure changes in the internal diameter of the JV or CA, from which changes of pressure in the vessel due to pulsatile blood flow can be inferred 10 , 11 . These methods require specialised, expensive equipment and an experienced ultrasound technologist to position the recording probe appropriately.…”

Section: Introductionmentioning

confidence: 99%

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Non-contact Quantification of Jugular Venous Pulse Waveforms from Skin Displacements

Tang

HajiRassouliha

Nash

et al. 2018

Sci Rep

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The jugular venous (JV) pressure waveform is a non-invasive, proven indicator of cardiovascular disease. Conventional clinical methods for assessing these waveforms are often overlooked because they require specialised expertise, and are invasive and expensive to implement. Recently, image-based methods have been used to quantify JV pulsation waveforms on the skin as an indirect way of estimating the pressure waveforms. However, these existing image-based methods cannot explicitly measure skin deformations and rely on the use of photoplethysmography (PPG) devices for identification of the pulsatile waveforms. As a result, they often have limited accuracy and robustness and are unsuitable in the clinical environment. Here, we propose a technique to directly measure skin deformations caused by the JV pulse using a very accurate subpixel registration algorithm. The method simply requires images obtained from the subject’s neck using a commodity camera. The results show that our measured waveforms contained all of the essential features of diagnostic JV waveforms in all of 19 healthy subjects tested in this study, indicating a significantly important capability for a potential future diagnostic device. The shape of our measured JV displacement waveforms was validated using waveforms measured with a laser displacement sensor, where the average correlation score between the two waveforms was 0.93 ± 0.05. In addition, synchronously recorded ECG signals were used to verify the timings of diagnostic features of the measured waveforms. To our knowledge, this is the first use of image registration for direct measurement of JV displacement waveforms. Significant advantages of our novel method include the high precision of our measurements, and the ability to use ordinary cameras, such as those in modern mobile phones. These advantages will enable the development of affordable and accessible devices to measure JV waveforms for cardiac diagnostics in the clinical environment. Future devices based on this technology may provide viable options for telemedicine applications, point of care diagnostics, and mobile-based cardiac health monitoring systems.

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Section: Introductionmentioning

confidence: 99%

“…The method of Moco et al . 11 required non-uniform illumination of the neck to increase variations in the frame-to-frame contrast of the moving regions. This method involves some manual steps, and has not yet been validated against an independent reference method.…”

Section: Introductionmentioning

confidence: 99%

Non-contact Quantification of Jugular Venous Pulse Waveforms from Skin Displacements

Tang

HajiRassouliha

Nash

et al. 2018

Sci Rep

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“…The central aortic pressure (CAP) reflected the true load imposed on the heart and large arteries rather than peripheral arteries [ 33 ]. However, the pressure signals of the central arteries must be extracted from the further transformation of alternative waveforms for non-invasive measurement techniques [ 26 ]. Compared to branchial and radial arteries, the waveform morphology of the carotid artery was similar to the aorta owing to the site near the aortic artery with lower pressure distortion [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

“…Equation (5) can be simplified from Equation (4) by assuming that Z basal is much smaller than Z pulsation due to the larger cross-sectional area of A basal than ΔA [ 26 ] under the consistent blood resistivity and measured length of the arterial segment:

…”

Section: Methodsmentioning

confidence: 99%

Bio-Impedance Measurement Optimization for High-Resolution Carotid Pulse Sensing

Wang

Chu

Chou

et al. 2021

Sensors

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Continuous hemodynamic monitoring is important for long-term cardiovascular healthcare, especially in hypertension. The impedance plethysmography (IPG) based carotid pulse sensing is a non-invasive diagnosis technique for measuring pulse signals and further evaluating the arterial conditions of the patient such as continuous blood pressure (BP) monitoring. To reach the high-resolution IPG-based carotid pulse detection for cardiovascular applications, this study provides an optimized measurement parameter in response to obvious pulsation from the carotid artery. The influence of the frequency of excitation current, electrode cross-sectional area, electrode arrangements, and physiological site of carotid arteries on IPG measurement resolution was thoroughly investigated for optimized parameters. In this study, the IPG system was implemented and installed on the subject’s neck above the carotid artery to evaluate the measurement parameters. The measurement results within 6 subjects obtained the arterial impedance variation of 2137 mΩ using the optimized measurement conditions, including excitation frequency of 50 kHz, a smaller area of 2 cm2, electrode spacing of 4 cm and 1.7 cm for excitation and sensing functions, and location on the left side of the neck. The significance of this study demonstrates an optimized measurement methodology of IPG-based carotid pulse sensing that greatly improves the measurement quality in cardiovascular monitoring.

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“…When using non-invasive methods of pressure measurement, various models arise that relate a hemodynamic parameter to BP. The correlation of blood flow and/or artery distension with BP and different mathematical models that relate these arterial variables to the arterial P W BP have been demonstrated [7]- [11].…”

Section: Introductionmentioning

confidence: 99%

Blood Pressure Models for Wearable Sensors

Seabra¹,

Silva²,

Rey³

2021

Preprint

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<div>As cardiovascular diseases are one of the most prominent illnesses, a continuous, non-invasive, and comfortable monitoring of blood pressure (BP) is indispensable. This paper investigates the best method for obtaining highly accurate BP values in non-invasive measurements when using an ultrasound (US) sensor projected for a wrist-worn device. State-of-the-art BP models were analyzed and qualitatively compared. Relevant arterial parameters such as luminal area, flow velocity and pulse wave velocity, of 729 subjects were extracted from a computer simulated database and served as input parameters for the wearable US. A linear in-silico model calibrated to each arterial-site revealed to be most accurate model. The linear model was used for the extraction of BP by using the US sensor and validated with a commercial pressure sensor in an ex-vivo experimental setup. The results showed an in-silico pulse pressure correlation of 0.978 and mean difference of (-2.134±2.477) mmHg at the radial artery and ex-vivo pressure correlation of 0.994 and mean difference of (0.554 ± 2.315) mmHg. Thus, with the linear model, the US measurement complies with the Association for the Advancement of Medical Instrumentation standard with deviations lower than 5 mmHg.</div>

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Assessing the blood pressure waveform of the carotid artery using an ultrasound image processing method

Cited by 15 publications

References 36 publications

Non-contact Quantification of Jugular Venous Pulse Waveforms from Skin Displacements

Non-contact Quantification of Jugular Venous Pulse Waveforms from Skin Displacements

Bio-Impedance Measurement Optimization for High-Resolution Carotid Pulse Sensing

Blood Pressure Models for Wearable Sensors

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