Innovative non-invasive microvascular assessment

公共卫生面临的一个主要挑战是心脏代谢和相关疾病，如代谢综合征、糖尿病和肥胖症的发病率和流行率增加。在微血管血流中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措施，并可能有助于其早期检测或治疗。激光多普勒血流仪（LDF）的测量体积约为1 mm 3，迄今已被广泛用于提供一种非侵入性技术，用于研究红细胞的运动和评估微血管功能和可能的疾病状态。然而，该方法仅提供血液灌注的相对指数，并且在测量不同皮肤部位时可能缺乏精确度和可重复性。激光散斑对比成像（LCSI）允许从更大的皮肤区域进行测量（约7 cm x 7 cm）产生血流的空间和时间测量，并在本项目中进行研究，以评估该技术是否可用于提供改进的诊断或定量描述能力。主要研究问题是确定是否存在与微血管功能障碍的已知机制相对应的血流模式，疾病状态以及这些是否可以用作对疾病风险进行分类或分层的特征。特别是，在连续LCSI中可用的更大的测量区域的效用将被调查，以测试这是否可以用来克服LDF的一些限制，包括测量站点specificity.The的操作和测量特性的LCSI将被评估，并确定其对测量数据的影响。将在时域和频域中评价来自健康和疾病组（例如，患有或不患有糖尿病（1型或2型））的基线信号（静息）和对激发/刺激（例如，动脉闭塞、加热、冷却）的响应的数据，并与LDF实现的数据进行比较。将考虑LSCI领域感兴趣区域的大小和位置，并开发技术以帮助或自动选择。信息理论的信号处理方法（Lempel-Ziv复杂性，样本熵和吸引子重建）将被开发和使用，以提供功能的分类schemes.The新的工程内容，这项研究来自信息理论的方法的开发和使用的数字信号处理和他们的适应帐户的多个时间尺度的不同过程调制皮肤血流。LSCI的使用也将促进时空复杂性分析的新用途，以阐明微血管床的系统级行为的进一步理解。这种方法在生物信号处理的许多领域都很有意义，但在许多其他工程领域也有潜在的应用，如健康和状态监测、交通流量和系统理论。医疗保健技术主题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