Signal Processing for Tissue Glucose Sensor

描述(由申请人提供)： 用于糖尿病患者的连续植入式葡萄糖传感器将通过常规注射、机械胰岛素泵或其他方式提供低血糖警告和改善胰岛素替代的信息。我们已经开发出了一种显示出希望的传感器。该传感器采用固定化葡萄糖氧化酶与恒电位氧电极相耦合。我们已经在狗身上使用了一个版本的传感器作为中心静脉植入物超过100天，而不需要重新校准，其他小组也报告了在人类血管内应用另一个版本的类似结果。这些成功是由新颖的传感器设计和固有稳定的酶的使用相结合实现的。然而，在组织中的应用有望更安全和更令人满意，这就需要开发新的信号处理技术。这项第一阶段研究的目标是开发新的信号处理算法，适用于植入复杂组织环境中的传感器。在第一阶段，我们将利用我们之前从植入动物模型的设备中获得的数据来开发必要的算法。在第二阶段，信号处理算法将有望应用于广泛的、新的动物植入评估。这项工作代表了对以前工作的重大创新，是实现长期植入式组织葡萄糖传感器的关键一步。

DESCRIPTION (provided by applicant): A continuous, implanted glucose sensor for diabetics would provide warnings of hypoglycemia and information for improved insulin replacement either by conventional injection, mechanical insulin pumps, or other means. We have already developed a sensor that shows promise. The sensor employs immobilized glucose oxidase coupled to a potentiostatic oxygen electrode. We have employed one version of the sensor as a central venous implant in dogs for over 100 days without the need for recalibration, and similar results from intravascular application in humans with another version have been reported by other groups. These successes were achieved by combinations of novel sensor design and the use of inherently stable enzymes. However, application in tissues, which is expected to be safer and more desirable, requires the development of new signal processing techniques. The goal for this Phase I study is to develop novel signal processing algorithms suitable for sensors implanted in a complex tissue environment. In Phase I, we will utilize data we have previously obtained from our devices implanted in animal models to develop the necessary algorithms. In Phase II, the signal processing algorithms will be applied prospectively in extensive, new animal implant evaluations. This work represents significant innovation over previous work and is a crucial step toward implementation of a long-term implantable tissue-based glucose sensor.