Stephen Edward Greenwald
@qmul.ac.uk
Blizard Institute, School of Medicine & Dentistry
Queen Mary University of London
RESEARCH, TEACHING, or OTHER INTERESTS
Biomedical Engineering, Cardiology and Cardiovascular Medicine
Scopus Publications
Scholar Citations
Scholar h-index
Scholar i10-index
Scopus Publications
Yonghui Wang, Yifan Zhang, Lisheng Xu, Shouliang Qi, Yudong Yao, Wei Qian, Stephen E. Greenwald, and Lin Qi
Springer Science and Business Media LLC
Xiaogang Li, Lianchang Ji, Rongrong Zhang, Hongrui You, Lisheng Xu, Stephen E. Greenwald, Yu Sun, Libo Zhang, and Benqiang Yang
Wiley
AbstractBackgroundThe curved planar reformation (CPR) technique is one of the most commonly used methods in clinical practice to locate coronary arteries in medical images.PurposeThe artery centerline is the cornerstone for the generation of the CPR image. Here, we describe the development of a new fully automatic artery centerline tracker with the aim of increasing the efficiency and accuracy of the process.MethodsWe propose a COronary artery Centerline Tracker (COACT) framework which consists of an ostium point finder (OPFinder) model, an intersection point detector (IPDetector) model and a set of centerline tracking strategies. The output of OPFinder is the ostium points. The function of the IPDetector is to predict the intersections of a sample sphere and the centerlines. The centerline tracking process starts from two ostium points detected by the OPFinder, and combines the results of the IPDetector with a series of strategies to gradually reconstruct the coronary artery centerline tree.ResultsTwo coronary CT angiography (CCTA) datasets were used to validate the models. Dataset1 contains 160 cases (32 for test and 128 for training) and dataset2 contains 70 cases (20 for test and 50 for training). The results show that the average distance between the ostium points predicted by the OPFinder and the manually annotated ostium points was 0.88 mm, which is similar to the differences between the results obtained by two observers (0.85 mm). For the IPDetector, the average overlap of the predicted and ground truth intersection points was 97.82% and this is also close to the inter‐observer agreement of 98.50%. For the entire coronary centerline tree, the overlap between the results obtained by COACT and the gold standard was 94.33%, which is slightly lower than the inter‐observer agreement, 98.39%.ConclusionsWe have developed a fully automatic centerline tracking method for CCTA scans and achieved a satisfactory result. The proposed algorithms are also incorporated in the medical image analysis platform TIMESlice (https://slice‐doc.netlify.app) for further studies.
Qianjin Wang, Lisheng Xu, Lu Wang, Xiaofan Yang, Yu Sun, Benqiang Yang, and Stephen E. Greenwald
Frontiers Media SA
Coronary artery segmentation is an essential procedure in the computer-aided diagnosis of coronary artery disease. It aims to identify and segment the regions of interest in the coronary circulation for further processing and diagnosis. Currently, automatic segmentation of coronary arteries is often unreliable because of their small size and poor distribution of contrast medium, as well as the problems that lead to over-segmentation or omission. To improve the performance of convolutional-neural-network (CNN) based coronary artery segmentation, we propose a novel automatic method, DR-LCT-UNet, with two innovative components: the Dense Residual (DR) module and the Local Contextual Transformer (LCT) module. The DR module aims to preserve unobtrusive features through dense residual connections, while the LCT module is an improved Transformer that focuses on local contextual information, so that coronary artery-related information can be better exploited. The LCT and DR modules are effectively integrated into the skip connections and encoder-decoder of the 3D segmentation network, respectively. Experiments on our CorArtTS2020 dataset show that the dice similarity coefficient (DSC), Recall, and Precision of the proposed method reached 85.8%, 86.3% and 85.8%, respectively, outperforming 3D-UNet (taken as the reference among the 6 other chosen comparison methods), by 2.1%, 1.9%, and 2.1%.
Jonghyok Ri, Na Pang, Shi Bai, Jialin Xu, Lisheng Xu, Songchol Ri, Yudong Yao, and Stephen E. Greenwald
AIP Publishing
Understanding the stress patterns produced by microbubbles (MB) in blood vessels is important in enhancing the efficacy and safety of ultrasound-assisted therapy, diagnosis, and drug delivery. In this study, the wall stress produced by the non-spherical oscillation of MBs within the lumen of micro-vessels was numerically analyzed using a three-dimensional finite element method. We systematically simulated configurations containing an odd number of bubbles from three to nine, equally spaced along the long axis of the vessel, insonated at an acoustic pressure of 200 kPa. We observed that 3 MBs were sufficient to simulate the stress state of an infinite number of bubbles. As the bubble spacing increased, the interaction between them weakened to the point that they could be considered to act independently. In the relationship between stress and acoustic frequency, there were differences between the single and 3 MB cases. The stress induced by 3 MBs was greater than the single bubble case. When the bubbles were near the wall, the shear stress peak was largely independent of vessel radius, but the circumferential stress peak increased with the radius. This study offers further insight into our understanding of the magnitude and distribution of stresses produced by multiple ultrasonically excited MBs inside capillaries.
Lisheng Xu, Shuran Zhou, Lu Wang, Yang Yao, Liling Hao, Lin Qi, Yudong Yao, Hongguang Han, Ramakrishna Mukkamala, and Stephen E. Greenwald
Springer Science and Business Media LLC
AbstractArterial stiffness, as measured by pulse wave velocity, for the early non-invasive screening of cardiovascular disease is becoming ever more widely used and is an independent prognostic indicator for a variety of pathologies including arteriosclerosis. Carotid-femoral pulse wave velocity (cfPWV) is regarded as the gold standard for aortic stiffness. Existing algorithms for cfPWV estimation have been shown to have good repeatability and accuracy, however, further assessment is needed, especially when signal quality is compromised. We propose a method for calculating cfPWV based on a simplified tube-load model, which allows for the propagation and reflection of the pulse wave. In-vivo cfPWV measurements from 57 subjects and numerical cfPWV data based on a one-dimensional model were used to assess the method and its performance was compared to three other existing approaches (waveform matching, intersecting tangent, and cross-correlation). The cfPWV calculated using the simplified tube-load model had better repeatability than the other methods (Intra-group Correlation Coefficient, ICC = 0.985). The model was also more accurate than other methods (deviation, 0.13 ms−1) and was more robust when dealing with noisy signals. We conclude that the determination of cfPWV based on the proposed model can accurately and robustly evaluate arterial stiffness.
Yu Sun, Xiao-gang Li, Kai Xu, Jie Hou, Hong-rui You, Rong-rong Zhang, Miao Qi, Li-bo Zhang, Li-sheng Xu, Stephen E. Greenwald,et al.
Springer Science and Business Media LLC
AbstractBackgroundThe ideal treatment strategy for stable three-vessel coronary artery disease (CAD) patients are difficult to determine and for patients undergoing conservative treatment, imaging evidence of coronary atherosclerotic severity progression remains limited. Epicardial fat volume (EFV) on coronary CT angiography (CCTA) has been considered to be associated with coronary atherosclerosis. Therefore, this study aims to evaluate the relationship between EFV level and coronary atherosclerosis severity in three-vessel CAD.MethodsThis retrospective study enrolled 252 consecutive patients with three-vessel CAD and 252 normal control group participants who underwent CCTA between January 2018 and December 2019. A semi-automatic method was developed for EFV quantification on CCTA images, standardized by body surface area. Coronary atherosclerosis severity was evaluated and scored by the number of coronary arteries with ≥ 50% stenosis on coronary angiography. Patients were subdivided into groups on the basis of lesion severity: mild (score = 3 vessels, n = 85), moderate (3.5 vessels ≤ score < 4 vessels, n = 82), and severe (4 vessels ≤ score ≤ 7 vessels, n = 85). The independent samplet-test, analysis of variance, and logistic regression analysis were used to evaluate the associations between EFV level and severity of coronary atherosclerosis.ResultsCompared with normal controls, three-vessel CAD patients had significantly higher EFV level (65 ± 22 mL/m2vs. 48 ± 19 mL/m2;P < 0.001). In patients with three-vessel CAD, there was a progressive decline in EFV level as the score of coronary atherosclerosis severity increased, especially in those patients with a body mass index (BMI) ≥ 25 kg/m2(75 ± 21 mL/m2vs. 72 ± 22 mL/m2vs. 62 ± 17 mL/m2;P < 0.05). Multivariable regression analysis showed that both BMI (OR3.40, 95% CI 2.00–5.78,P < 0.001) and the score of coronary atherosclerosis severity (OR0.49, 95% CI 0.26–0.93,P < 0.05) were independently related to the change of EFV level.ConclusionThree-vessel CAD patients do have higher EFV level than the normal controls. While, there may be an inverse relationship between EFV level and the severity of coronary atherosclerosis in patients with three-vessel CAD.
Sijung Hu, Steve Greenwald, Vincent Dwyer, Janis Spigulis, and Yutao Guo
Frontiers Media SA
This edition of Frontiers in Physics is focussed on the research topic “In Vivo OptoPhysiological Imaging” and emphasises the value of delivering high performance multifunctional in vivo imaging and its dependence on the development of sophisticated electronics, executive software and, increasingly, the application of machine learning (ML) techniques to a diverse range of signal processing algorithms. Over the last half century there has been a steady increase in the diversity of optical techniques used to distinguish normal from pathological function, and these have provided an ever-widening range of diagnostic means for monitoring treatment effectiveness. In this special edition of Frontiers in Physics, two papers from Arrigo and co-workers Arrigo et al., Arrigo et al. show how developments in optical coherence tomography (OCT) hardware and image processing are making it possible to quantify neovascularisation associated with age-related macular degeneration (AMD); thus affording new ways to investigate its pathogenesis, development and likely outcome. This troubling disease, with a prevalence in Europe of over 2% [1], is an increasing burden in countries with ageing populations [2]. The basic idea in Arrigo et al. is to understand why OCT does not reveal the full extent of neovascular lesions in AMD, especially in the type I variant. The performance of OCT in this context is also limited by differences between manufacturers and experimental protocols. Given these constraints, the aim of this study was to distinguish between high and low reflectivity lesions (as a measure of well or poorly detected blood flow) and to see the relationship between OCT performance and blood flow when compared to the gold standard of angiography, (either using fluorescein or indocyanine green, ICG). By measuring average lesion area from images of 50 eyes (beautiful images judging by the representative examples presented), the authors show that the agreement between lesion areas measured by OCT and angiography is remarkably close for type II lesions, while for type 1 lesions, the agreement is good only when using early ICG angiography OPEN ACCESS
Along Song, Lisheng Xu, Lu Wang, Bin Wang, Xiaofan Yang, Bu Xu, Benqiang Yang, and Stephen E. Greenwald
Institute of Electrical and Electronics Engineers (IEEE)
Automatic coronary artery segmentation is of great value in diagnosing coronary disease. In this paper, we propose an automatic coronary artery segmentation method for coronary computerized tomography angiography (CCTA) images based on a deep convolutional neural network. The proposed method consists of three steps. First, to improve the efficiency and effectiveness of the segmentation, a 2D DenseNet classification network is utilized to screen out the non-coronary-artery slices. Second, we propose a coronary artery segmentation network based on the 3D-UNet, which is capable of extracting, fusing and rectifying features efficiently for accurate coronary artery segmentation. Specifically, in the encoding process of the 3D-UNet network, we adapt the dense block into the 3D-UNet so that it can extract rich and representative features for coronary artery segmentation; In the decoding process, 3D residual blocks with feature rectification capability are applied to improve the segmentation quality further. Third, we introduce a Gaussian weighting method to obtain the final segmentation results. This operation can highlight the more reliable segmentation results at the center of the 3D data blocks while weakening the less reliable segmentations at the block boundary when merging the segmentation results of spatially overlapping data blocks. Experiments demonstrate that our proposed method achieves a Dice Similarity Coefficient (DSC) value of 0.826 on a CCTA dataset constructed by us. The code of the proposed method is available at https://github.com/alongsong/3D_CAS.
Yang Yao, Shuran Zhou, Jordi Alastruey, Liling Hao, Stephen E. Greenwald, Yuelan Zhang, Lin Xu, Lisheng Xu, and Yudong Yao
Elsevier BV
Wenyan Liu, Shuo Du, Shuran Zhou, Tiemin Mei, Yuelan Zhang, Guozhe Sun, Shuang Song, Lisheng Xu, Yudong Yao, and Stephen E. Greenwald
Elsevier BV
Huiying Cui, Zhongyi Wang, Bin Yu, Fangfang Jiang, Ning Geng, Yongchun Li, Lisheng Xu, Dingchang Zheng, Biyong Zhang, Peilin Lu,et al.
MDPI AG
Ballistocardiography (BCG) is considered a good alternative to HRV analysis with its non-contact and unobtrusive acquisition characteristics. However, consensus about its validity has not yet been established. In this study, 50 healthy subjects (26.2 ± 5.5 years old, 22 females, 28 males) were invited. Comprehensive statistical analysis, including Coefficients of Variation (CV), Lin’s Concordance Correlation Coefficient (LCCC), and Bland-Altman analysis (BA ratio), were utilized to analyze the consistency of BCG and ECG signals in HRV analysis. If the methods gave different answers, the worst case was taken as the result. Measures of consistency such as Mean, SDNN, LF gave good agreement (the absolute value of CV difference < 2%, LCCC > 0.99, BA ratio < 0.1) between J-J (BCG) and R-R intervals (ECG). pNN50 showed moderate agreement (the absolute value of CV difference < 5%, LCCC > 0.95, BA ratio < 0.2), while RMSSD, HF, LF/HF indicated poor agreement (the absolute value of CV difference ≥ 5% or LCCC ≤ 0.95 or BA ratio ≥ 0.2). Additionally, the R-R intervals were compared with P-P intervals extracted from the pulse wave (PW). Except for pNN50, which exhibited poor agreement in this comparison, the performances of the HRV indices estimated from the PW and the BCG signals were similar.
Bu Xu, Benqiang Yang, Junrui Xiao, Along Song, Bin Wang, Lu Wang, Lisheng Xu, Stephen E. Greenwald, and Yudong Yao
Elsevier BV
Yang Cao, Wenyan Liu, Shuang Zhang, Lisheng Xu, Baofeng Zhu, Huiying Cui, Ning Geng, Hongguang Han, and Stephen E. Greenwald
Frontiers Media SA
PurposeMyocardial infarction (MI) is one of the most common cardiovascular diseases, frequently resulting in death. Early and accurate diagnosis is therefore important, and the electrocardiogram (ECG) is a simple and effective method for achieving this. However, it requires assessment by a specialist; so many recent works have focused on the automatic assessment of ECG signals.MethodsFor the detection and localization of MI, deep learning models have been proposed, but the diagnostic accuracy of this approaches still need to be improved. Moreover, with deep learning methods the way in which a given result was achieved lacks interpretability. In this study, ECG data was obtained from the PhysioBank open access database, and was analyzed as follows. Firstly, the 12-lead ECG signal was preprocessed to identify each beat and obtain each heart interval. Secondly, a multi-scale deep learning model combined with a residual network and attention mechanism was proposed, where the input was the 12-lead ECG recording. Through the SENet model and the Grad-CAM algorithm, the weighting of each lead was calculated and visualized. Using existing knowledge of the way in which different types of MI gave characteristic patterns in specific ECG leads, the model was used to provisionally diagnose the type of MI according to the characteristics of each of the 12 ECG leads.ResultsTen types of MI anterior, anterior lateral, anterior septal, inferior, inferior lateral, inferior posterior, inferior posterior lateral, lateral, posterior, and posterior lateral were diagnosed. The average accuracy, sensitivity, and specificity for MI detection of all lesion types was 99.98, 99.94, and 99.98%, respectively; and the average accuracy, sensitivity, and specificity for MI localization was 99.79, 99.88, and 99.98%, respectively.ConclusionWhen compared to existing models based on traditional machine learning methods, convolutional neural networks and recurrent neural networks, the results showed that the proposed model had better diagnostic performance, being superior in accuracy, sensitivity, and specificity.
Lina Shibib, Mo Al-Qaisi, Ahmed Ahmed, Alexander D Miras, David Nott, Marc Pelling, Stephen E Greenwald, and Nicola Guess
Informa UK Limited
Abstract Over the past 50 years, many countries around the world have faced an unchecked pandemic of obesity and type 2 diabetes (T2DM). As best practice treatment of T2DM has done very little to check its growth, the pandemic of diabesity now threatens to make health-care systems economically more difficult for governments and individuals to manage within their budgets. The conventional view has been that T2DM is irreversible and progressive. However, in 2016, the World Health Organization (WHO) global report on diabetes added for the first time a section on diabetes reversal and acknowledged that it could be achieved through a number of therapeutic approaches. Many studies indicate that diabetes reversal, and possibly even long-term remission, is achievable, belying the conventional view. However, T2DM reversal is not yet a standardized area of practice and some questions remain about long-term outcomes. Diabetes reversal through diet is not articulated or discussed as a first-line target (or even goal) of treatment by any internationally recognized guidelines, which are mostly silent on the topic beyond encouraging lifestyle interventions in general. This review paper examines all the sustainable, practical, and scalable approaches to T2DM reversal, highlighting the evidence base, and serves as an interim update for practitioners looking to fill the practical knowledge gap on this topic in conventional diabetes guidelines.
Chenfei Ma, Chuang Lin, Oluwarotimi Williams Samuel, Weiyu Guo, Hang Zhang, Steve Greenwald, Lisheng Xu, and Guanglin Li
Institute of Electrical and Electronics Engineers (IEEE)
In human-machine interaction systems, continuous movement estimation methods occupy an important position because they are more natural and intuitive than pattern-recognition methods. Essentially, arm position is decided by the shoulder and elbow joint angles. However, the various deformations of muscles around the shoulder and elbow often lead to difficulties in sensor fixation, which results in a loss of synchronization between the surface electromyography (sEMG) signals and joint angles. In order to accurately estimate movement angles using sEMG in situations where the sEMG is not synchronized with joint angles, we utilized a bi-directional long short-term memory (Bi-LSTM) network rather than other deep learning methods to estimate non-dominant arm movements, based on the sEMG signal from the dominant arm. This estimation protocol was designed to avoid a multiplicity of sensors and to simulate more complicated loss of synchronization problems). The performance of the Bi-LSTM was compared with multilayer perceptrons (MLPs), convolutional neural networks (CNNs), and a long short-term memory network (LSTM). The Pearson correlation coefficient (cc) between the estimated and target joint angle sequences was calculated to evaluate the performance of each neural network. The Wilcoxon signed-rank results showed that the Bi-LSTM model significantly outperformed the MLP, CNN, and LSTM models (tested with completely untrained newly recorded free movements).
Wenyan Liu, Zongpeng Li, Yufan Wang, Daiyuan Song, Ning Ji, Lisheng Xu, Tiemin Mei, Yingxian Sun, and Stephen E. Greenwald
Elsevier BV
Xiaogang Li, Yu Sun, Lisheng Xu, Stephen E. Greenwald, Libo Zhang, Rongrong Zhang, Hongrui You, and Benqiang Yang
Wiley
PurposeEpicardial fat is the adipose tissue between the serosal pericardial wall layer and the visceral layer. It is distributed mainly around the atrioventricular groove, atrial septum, ventricular septum and coronary arteries. Studies have shown that the density, thickness, volume and other characteristics of epicardial adipose tissue (EAT) are independently correlated with a variety of cardiovascular diseases. Given this association, the accurate determination of EAT volume is an essential aim of future research. Therefore, the purpose of this study was to establish a framework for fully automatic EAT segmentation and quantification in coronary computed tomography angiography (CCTA) scans.MethodsA set of 103 scans are randomly selected from our medical center. An automatic pipeline has been developed to segment and quantify the volume of EAT. First, a multi‐slice deep neural network is used to simultaneously segment the pericardium in multiple adjacent slices. Then a deformable model is employed to reduce false positive and negative regions in the segmented binary pericardial images. Finally, the pericardium mask is used to define the region of interest (ROI) and the threshold method is utilized to extract the pixels ranging from −175 Hounsfield units (HU) to −15 HU for the segmentation of EAT.ResultsThe Dice indices of the pericardial segmentation using the proposed method with respect to the manual delineation results of two radiology experts were 97.1% 0.7% and 96.9% 0.6%, respectively. The inter‐observer variability was also assessed, resulting in a Dice index of 97.0% 0.7%. For the EAT segmentation results, the Dice indices between the proposed method and the two radiology experts were 93.4% 1.5% and 93.3% 1.3%, respectively, and the same measurement between the experts themselves was 93.6% 1.9%. The Pearson’s correlation coefficients between the EAT volumes computed from the results of the proposed method and the manual delineation by the two experts were 1.00 and 0.99 and the same coefficients between the experts was 0.99.ConclusionsThis work describes the development of a fully automatic EAT segmentation and quantification method from CCTA scans and the results compare favorably with the assessments of two independent experts. The proposed method is also packaged with a graphical user interface which can be found at https://github.com/MountainAndMorning/EATSeg.
Marta S. Godinho, Chavaunne T. Thorpe, Steve E. Greenwald, and Hazel R.C. Screen
Elsevier BV
Dingchang Zheng, Yang Yao, Iona Morrison, and Steve Greenwald
Elsevier
Boyuan Zhang, Hengkang Li, Lisheng Xu, Lin Qi, Yudong Yao, and Stephen E. Greenwald
Hindawi Limited
Remote photoplethysmography (rPPG) can be used for noncontact and continuous measurement of the heart rate (HR). Currently, the main factors affecting the accuracy and robustness of rPPG-based HR measurement methods are the subject’s skin tone, body movement, exercise recovery, and variable or inadequate illumination. In response to these challenges, this study is aimed at investigating a rPPG-based HR measurement method that is effective under a wide range of conditions by only using a webcam. We propose a new approach, which combines joint blind source separation (JBSS) and a projection process based on a skin reflection model, so as to eliminate the interference of background illumination and enhance the extraction of pulse rate information. Three datasets derived from subjects with different skin tones considering six environmental scenarios are used to validate the proposed method against three other state-of-the-art methods. The results show that the proposed method can provide more accurate and robust HR measurement for all three datasets and is therefore more applicable to a wide range of scenarios.
Zeyang Zhu, Jianhua Li, Shuang Zhang, Ning Geng, Lisheng Xu, and Stephen E Greenwald
IOP Publishing
Abstract Objective: Portable devices for collecting electrocardiograms (ECGs) and telemedicine systems for diagnosis are available to residents in deprived areas, but ECGs collected by non-professionals are not necessarily reliable and may impair the accuracy of diagnosis. We propose an algorithm for accurate ECG quality assessment, which can help improve the reliability of ECGs collected by portable devices. Approach: Using challenge data from CinC (2019), signals were classified as ‘acceptable’ and ‘unacceptable’ by annotators. The training set contained 998 12-lead ECGs and the test set contained 500. A 998 × 84 feature matrix, S, was formed by feature extraction and three basic models were obtained through training SVM, DT and NBC on S. The feature subsets S1, S2 and S3 were obtained by dimensionality reduction on S using SVM, DT and NBC, respectively. Three other basic models were obtained through training SVM on S1, DT on S2 and NBC on S3. By combining these six basic models, several integrated models were formed. An iterative method was proposed to select the integrated model with the highest accuracy on the training set. Having compared differences between the output labels and the original data labels, evaluation criteria were calculated. Main results: An accuracy of 98.70% and 98.60% was achieved on the training and test datasets, respectively. High F1 score and Kappa values were also obtained. Significance: The proposed algorithm has advantages over previously reported approaches during automatic assessment of ECG quality and can thus help to reduce reliance on highly trained professionals when assessing the quality of ECGs.
Yanlu Li, Louise Marais, Hakim Khettab, Zhiheng Quan, Soren Aasmul, Rob Leinders, Ralf Schüler, Padraic E. Morrissey, Stephen Greenwald, Patrick Segers,et al.
Optica Publishing Group
Pulse wave velocity (PWV) is a reference measure for aortic stiffness, itself an important biomarker of cardiovascular risk. To enable low-cost and easy-to-use PWV measurement devices that can be used in routine clinical practice, we have designed several handheld PWV sensors using miniaturized laser Doppler vibrometer (LDV) arrays in a silicon photonics platform. The LDV-based PWV sensor design and the signal processing protocol to obtain pulse transit time (PTT) and carotid-femoral PWV in a feasibility study in humans, are described in this paper. Compared with a commercial reference PWV measurement system, measuring arterial pressure waveforms by applanation tonometry, LDV-based displacement signals resulted in more complex signals. However, we have shown that it is possible to identify reliable fiducial points for PTT calculation using the maximum of the 2nd derivative algorithm in LDV-based signals, comparable to those obtained by the reference technique, applanation tonometry.
Hongju Wang, Lu Wang, Nannan Sun, Yang Yao, Liling Hao, Lisheng Xu, and Stephen E. Greenwald
Frontiers Media SA
The accurate measurement of the arterial pulse wave is beneficial to clinical health assessment and is important for the effective diagnosis of many types of cardiovascular disease. A variety of sensors have been developed for the non-invasive detection of these waves, but the type of sensor has an impact on the measurement results. Therefore, it is necessary to compare and analyze the signals obtained under a range of conditions using various pulse sensors to aid in making an informed choice of the appropriate type. From the available types we have selected four: a piezoresistive strain gauge sensor (PESG) and a piezoelectric Millar tonometer (the former with the ability to measure contact force), a circular film acceleration sensor, and an optical reflection sensor. Pulse wave signals were recorded from the left radial, carotid, femoral, and digital arteries of 60 subjects using these four sensors. Their performance was evaluated by analyzing their susceptibilities to external factors (contact force, measuring site, and ambient light intensity) and by comparing their stability and reproducibility. Under medium contact force, the peak-to-peak amplitude of the signals was higher than that at high and low force levels and the variability of signal waveform was small. The optical sensor was susceptible to ambient light. Analysis of the intra-class correlation coefficients (ICCs) of the pulse wave parameters showed that the tonometer and accelerometer had good stability (ICC > 0.80), and the PESG and optical sensor had moderate stability (0.46 < ICC < 0.86). Intra-observer analysis showed that the tonometer and accelerometer had good reproducibility (ICC > 0.75) and the PESG and optical sensor had moderate reproducibility (0.42 < ICC < 0.91). Inter-observer analysis demonstrated that the accelerometer had good reproducibility (ICC > 0.85) and the three other sensors had moderate reproducibility (0.52 < ICC < 0.96). We conclude that the type of sensor and measurement site affect pulse wave characteristics and the careful selection of appropriate sensor and measurement site are required according to the research and clinical need. Moreover, the influence of external factors such as contact pressure and ambient light should be fully taken into account.
Lisheng Xu, Yanling Zhang, Long Meng, Shuai Li, Lili Niu, Stephen E. Greenwald, Hui Zhang, and Lu Wang
Institute of Electrical and Electronics Engineers (IEEE)
Mechanical factors, especially wall shear stress (WSS) and circumferential strain (CS), play an important role in the progression and rupture of atherosclerotic plaques. Previous studies have shown that the temporal phase angle between WSS and CS, referred to as the stress phase angle (SPA) may be a biomarker for plaque development and vulnerability. Since the SPA is influenced by the severity and length of the stenosis, a multifactorial relationship between local hemodynamic variables and plaque morphology can be hypothesized. However, due to ethical restrictions and the difficulty of developing animal models, there is little experimental data to support such a hypothesis about the biomarker function of SPA. In this study, a novel non-invasive ultrasound-based method for investigating the relationship between local hemodynamics and plaque morphology is developed and investigated in-vitro and in-vivo with an apolipoprotein deficient mouse model. In the in-vitro experiments, using polyvinyl alcohol cryogel (PVA-c) phantoms, we have observed that the SPA becomes more negative and the wall shear rate (WSR) rises, as the severity of the stenosis increases. Conversely, SPA becomes more positive and WSR falls with increasing plaque length. These changes in plaque morphology have little effect on CS. The in-vivo experiments show that, as the severity of the plaque increases, the SPA becomes more negative and WSR tends to more positive values; whereas, the CS drops. We conclude that these local hemodynamic parameters such as SPA, WSR and CS, as suggested by others, can be regarded as prognostic markers for the assessment of plaque vulnerability.
Konstantinos Petkos, Simos Koutsoftidis, Thomas Guiho, Patrick Degenaar, Andrew Jackson, Stephen E. Greenwald, Peter Brown, Timothy Denison, and Emmanuel M. Drakakis
Springer Science and Business Media LLC
Abstract Background It is widely accepted by the scientific community that bioelectrical signals, which can be used for the identification of neurophysiological biomarkers indicative of a diseased or pathological state, could direct patient treatment towards more effective therapeutic strategies. However, the design and realisation of an instrument that can precisely record weak bioelectrical signals in the presence of strong interference stemming from a noisy clinical environment is one of the most difficult challenges associated with the strategy of monitoring bioelectrical signals for diagnostic purposes. Moreover, since patients often have to cope with the problem of limited mobility being connected to bulky and mains-powered instruments, there is a growing demand for small-sized, high-performance and ambulatory biopotential acquisition systems in the Intensive Care Unit (ICU) and in High-dependency wards. Finally, to the best of our knowledge, there are no commercial, small, battery-powered, wearable and wireless recording-only instruments that claim the capability of recording electrocorticographic (ECoG) signals. Methods To address this problem, we designed and developed a low-noise (8 nV/√Hz), eight-channel, battery-powered, wearable and wireless instrument (55 × 80 mm2). The performance of the realised instrument was assessed by conducting both ex vivo and in vivo experiments. Results To provide ex vivo proof-of-function, a wide variety of high-quality bioelectrical signal recordings are reported, including electroencephalographic (EEG), electromyographic (EMG), electrocardiographic (ECG), acceleration signals, and muscle fasciculations. Low-noise in vivo recordings of weak local field potentials (LFPs), which were wirelessly acquired in real time using segmented deep brain stimulation (DBS) electrodes implanted in the thalamus of a non-human primate, are also presented. Conclusions The combination of desirable features and capabilities of this instrument, namely its small size (~one business card), its enhanced recording capabilities, its increased processing capabilities, its manufacturability (since it was designed using discrete off-the-shelf components), the wide bandwidth it offers (0.5–500 Hz) and the plurality of bioelectrical signals it can precisely record, render it a versatile and reliable tool to be utilized in a wide range of applications and environments.