Innovative non-invasive microvascular assessment

公共卫生面临的一大挑战是心脏代谢和相关疾病的发病率和流行率增加，例如代谢综合征、糖尿病和肥胖症。微血管血流中可观察到的自发振荡的变化可能先于这些疾病中发生的自主神经功能障碍的其他指标，并可能有助于它们的早期发现或治疗。激光多普勒血流计(LDF)的测量体积约为1mm3，迄今已被广泛应用，为研究红细胞运动和评估微血管功能及可能的疾病状态提供了一种非侵入性技术。然而，这种方法只提供了一个相对的血流灌注指数，当测量不同的皮肤部位时，缺乏精确度和重复性。激光散斑对比成像(LCSI)可以从更大的皮肤区域(约7 cm x 7 cm)测量血流的空间和时间，并在本项目中进行研究，以评估该技术是否可以用于提供更好的诊断或定量描述能力。主要研究问题是确定血流中是否存在与疾病状态下微血管功能障碍的已知机制相对应的模式，以及这些模式是否可以用作分类或分层疾病风险的特征。特别是，将调查连续LCSI中可用的更大测量区域的效用，以测试这是否可以用来克服LDF的一些限制，包括测量地点的特殊性。将评估LCSI的操作和测量特性，并确定它们对测量数据的影响。来自健康和疾病组(例如，合并或不合并糖尿病(1型或2型))的基线信号(静态)和对挑战/刺激(例如，动脉闭塞、加热、降温)的反应的数据将在时间和频率域中进行评估，并与LDF的结果进行比较。将考虑LSCI领域中感兴趣区域的大小和位置，并开发技术来帮助或自动选择它们。信号处理的信息论方法(Lempel-Ziv复杂性、样本熵和吸引子重建)将被开发并用于提供分类模式的特征。本研究的新的工程学内容来自于发展和使用数字信号处理的信息论方法及其适应不同调节皮肤血流的不同过程的多时间尺度。LSCI的使用还将促进时空复杂性分析的新应用，以阐明对微血管床系统水平行为的进一步理解。这种方法在生物信号处理的许多领域都很有兴趣，但在许多其他工程领域也有潜在的应用，如健康和状况监测、交通流量和系统理论。医疗技术临床技术数字信号处理

A major challenge facing public health is the increased incidence and prevalence of cardio-metabolic and related diseases, e.g. metabolic syndrome, diabetes and obesity. Changes in the spontaneous oscillations observable in microvascular blood flow may precede other measures of autonomic dysfunction that occur in these diseases and could help in their early detection or treatment. Laser Doppler flowmetry (LDF), with a measurement volume of approximately 1mm3, has been extensively used to date to provide a non-invasive technique for investigating the motion of red blood cells and the assessment of microvascular function and possible disease state. However, this method provides only a relative index of blood perfusion and can lack precision and repeatability when measuring different skin sites. Laser speckle contrast imaging (LCSI) allows measurement from larger skin areas (approximately 7cm x 7cm) yielding spatial and temporal measurement of blood flow and is investigated in this project to assess if the technique can be used to provide improved diagnostic or quantitative descriptive capabilities.The primary research question is to determine if there are patterns in blood flow that correspond to known mechanisms of microvascular dysfunction in disease states and if these can be used as features to classify or stratify disease risk. In particular, the utility of the larger measurement area available in continuous LCSI will be investigated to test whether this can be used to overcome some of the limitations of LDF, including measurement site specificity.The operational and measurement characteristics of the LCSI will be evaluated and their influence on the measured data determined. Data from baseline signals (at rest) and response to challenges/stimuli (e.g. arterial occlusion, heating, cooling) in healthy and disease groups, (e.g. with or without diabetes (type 1 or 2)), will be evaluated in the time and frequency domain and compared with that achieved by LDF. The size and location of regions of interest in the LSCI field will be considered and techniques developed to aid or automate their selection. Information theoretic approaches to signal processing (Lempel-Ziv complexity, sample entropy and attractor reconstruction) will be developed and employed to provide features for classification schemes.The novel engineering content of this research derives from the development and use of information theoretic approaches to the digital signal processing and their adaptation to account for the multiple time-scales of the different processes modulating skin blood flow. The use of LSCI will also facilitate the novel use of spatiotemporal complexity analysis to elucidate further understanding of the system level behaviour of microvascular beds. Such approaches are of interest in many areas of bio-signal processing but have potential application in many other engineering fields such as health and condition monitoring, traffic flows and systems theory.Healthcare technologies themeClinical technologiesDigital signal processing