Research Centre for Biomedical Engineering, Department of Electrical and Electronic Engineering, School of Mathematics, Computer Science and Engineering, City, University of London, London, UK.
Sci Rep. 2020 Oct 9;10(1):16905. doi: 10.1038/s41598-020-73406-4.
Biochemical and medical literature establish lactate as a fundamental biomarker that can shed light on the energy consumption dynamics of the body at cellular and physiological levels. It is therefore, not surprising that it has been linked to many critical conditions ranging from the morbidity and mortality of critically ill patients to the diagnosis and prognosis of acute ischemic stroke, septic shock, lung injuries, insulin resistance in diabetic patients, and cancer. Currently, the gold standard for the measurement of lactate requires blood sampling. The invasive and costly nature of this procedure severely limits its application outside intensive care units. Optical sensors can provide a non-invasive, inexpensive, easy-to-use, continuous alternative to blood sampling. Previous efforts to achieve this have shown significant potential, but have been inconclusive. A measure that has been previously overlooked in this context, is the use of variable selection methods to identify regions of the optical spectrum that are most sensitive to and representative of the concentration of lactate. In this study, several wavelength selection methods are investigated and a new genetic algorithm-based wavelength selection method is proposed. This study shows that the development of more accurate and parsimonious models for optical estimation of lactate is possible. Unlike many existing methods, the proposed method does not impose additional locality constraints on the spectral features and therefore helps provide a much more granular interpretation of wavelength importance.
生化和医学文献将乳酸确立为一种基本的生物标志物，可以揭示细胞和生理水平上身体能量消耗的动态。因此，毫不奇怪，它与许多关键情况有关，从危重病患者的发病率和死亡率到急性缺血性中风、败血症性休克、肺损伤、糖尿病患者的胰岛素抵抗和癌症的诊断和预后。目前，乳酸测量的金标准需要进行血液采样。这种侵入性和昂贵的过程严重限制了它在重症监护室之外的应用。光学传感器可以提供一种非侵入性、低成本、易于使用、连续的替代血液采样的方法。以前为实现这一目标所做的努力显示出了巨大的潜力，但尚未得出明确的结论。在这种情况下，以前被忽视的一个措施是使用变量选择方法来识别对乳酸浓度最敏感和最具代表性的光学光谱区域。在这项研究中，研究了几种波长选择方法，并提出了一种新的基于遗传算法的波长选择方法。这项研究表明，有可能为光学估计乳酸开发更准确和简约的模型。与许多现有方法不同，所提出的方法不会对光谱特征施加额外的局部约束，因此有助于更细致地解释波长的重要性。
