Non-invasive technique for assessment of vascular wall stiffness using laser Doppler vibrometry

Campo, Adriaan; Segers, Patrick; Heuten, Hilde; Goovaerts, Inge; Ennekens, Guy; Vrints, Christiaan J.; Baets, Roel; Dirckx, Joris J.J.

doi:10.1088/0957-0233/25/6/065701

Cited by 16 publications

(15 citation statements)

References 28 publications

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“…Yanlu Li et al suggested a system with two laser Doppler vibrometry as a potential technique to measure the local carotid PWV in two locations along the common carotid artery from skin vibrations [ 69 ]. Recently, Adriaan Campo et al presented an instrument with a double laser Doppler vibrometer to assess the local PWV at the carotid site [ 70 ].…”

Section: Devicesmentioning

confidence: 99%

Novel Methods for Pulse Wave Velocity Measurement

2015

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The great incidence of cardiovascular (CV) diseases in the world spurs the search for new solutions to enable an early detection of pathological processes and provides more precise diagnosis based in multi-parameters assessment. The pulse wave velocity (PWV) is considered one of the most important clinical parameters for evaluate the CV risk, vascular adaptation, and therapeutic efficacy. Several studies were dedicated to find the relationship between PWV measurement and pathological status in different diseases, and proved the relevance of this parameter. The commercial devices dedicate to PWV estimation make a regional assessment (measured between two vessels), however a local measurement is more precise evaluation of artery condition, taking into account the differences in the structure of arteries. Moreover, the current devices present some limitations due to the contact nature. Emerging trends in CV monitoring are moving away from more invasive technologies to non-invasive and non-contact solutions. The great challenge is to explore the new instrumental solutions that allow the PWV assessment with fewer approximations for an accurately evaluation and relatively inexpensive techniques in order to be used in the clinical routine.

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Section: Devicesmentioning

confidence: 99%

Novel Methods for Pulse Wave Velocity Measurement

2015

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“…Therefore, it is possible to detect the pulse wave at multiple known locations along the trajectory of the vessel and to calculate the PWV. Previous experiments indicate that LDV can be used to measure cfPWV and local carotid PWV [10], [11]. In this study, we assess the feasibility of the 4-channel vibrometer system to assess PWV velocity of phantoms with varying architectural traits, mimicking gross arterial pathologies.…”

Section: Local Pulse Wave Velocity Detectionmentioning

confidence: 99%

Application of a new four-channel vibrometer for determination of atherosclerosis: Further advances towards a handheld device

Campo

Dirckx

Widman

et al. 2016

2016 IEEE International Symposium on Medical Measurements and Applications (MeMeA)

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Cardiovascular diseases (CD) are the leading cause of death worldwide and their prevalence is expected to rise. Important in the etiology of CD is the stiffening of the large arteries (arteriosclerosis) and plaque formation (atherosclerosis) in the common carotid artery (CCA). Increasing evidence shows that both arteriosclerosis and atherosclerosis can be detected by assessing pulse wave velocity (PWV) in the CCA, and several techniques focus on the detection of PWV in this structure. In previous studies, laser doppler vibrometry (LDV) was proposed as an approach to detect the arterial system. In the present work, a compact 4-channel LDV system is introduced for PWV detection. Four phantom arteries were assessed mimicking real life cardiovascular pathologies. Due to the high sensitivity and the increased spatial and temporal resolution of the LDV system, PWV could be assessed, and even local changes in phantom architecture could be detected. The system could potentially be use to detect arteriosclerosis and plaque during cardiovascular screening.

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“…Heart periods are computed from the R peaks of the ECG and the corresponding peaks in the LDV, which are identified by means of a semi-automatic algorithm based on [ 29 ]. Other researchers (e.g., [ 23 , 30 , 31 , 32 , 33 ]) focus on estimating the pulse-wave velocity, which measures the speed of propagation of pulse waves along the arterial tree. In this work, the analysis still relies on manual intervention and/or ECG signal availability.…”

Section: Introductionmentioning

confidence: 99%

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

Antognoli

Moccia

Migliorelli

et al. 2020

Sensors

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Background: Heartbeat detection is a crucial step in several clinical fields. Laser Doppler Vibrometer (LDV) is a promising non-contact measurement for heartbeat detection. The aim of this work is to assess whether machine learning can be used for detecting heartbeat from the carotid LDV signal. Methods: The performances of Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and K-Nearest Neighbor (KNN) were compared using the leave-one-subject-out cross-validation as the testing protocol in an LDV dataset collected from 28 subjects. The classification was conducted on LDV signal windows, which were labeled as beat, if containing a beat, or no-beat, otherwise. The labeling procedure was performed using electrocardiography as the gold standard. Results: For the beat class, the f1-score (f1) values were 0.93, 0.93, 0.95, 0.96 for RF, DT, KNN and SVM, respectively. No statistical differences were found between the classifiers. When testing the SVM on the full-length (10 min long) LDV signals, to simulate a real-world application, we achieved a median macro-f1 of 0.76. Conclusions: Using machine learning for heartbeat detection from carotid LDV signals showed encouraging results, representing a promising step in the field of contactless cardiovascular signal analysis.

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Non-invasive technique for assessment of vascular wall stiffness using laser Doppler vibrometry

Cited by 16 publications

References 28 publications

Novel Methods for Pulse Wave Velocity Measurement

Novel Methods for Pulse Wave Velocity Measurement

Application of a new four-channel vibrometer for determination of atherosclerosis: Further advances towards a handheld device

Heartbeat Detection by Laser Doppler Vibrometry and Machine Learning

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