Robust Signal Processing for Tissue Glucose Sensor

描述（由申请人提供）：一种用于糖尿病患者的连续植入血糖传感器，可提供低血糖警告，并通过常规注射、机械胰岛素泵或其他方式提供改进胰岛素替代的信息。我们已经开发出一种很有前景的传感器。该传感器采用固定化葡萄糖氧化酶偶联到恒电位氧电极。我们已经将一种版本的传感器作为中心静脉植入狗体内100多天，而无需重新校准，其他小组也报道了另一种版本的传感器在人类血管内应用的类似结果。这些成功是通过结合新型传感器设计和使用固有稳定的酶来实现的。然而，在组织中的应用，期望更安全，更理想，需要新的信号处理技术的发展。在第一阶段，我们开发了适用于植入复杂组织环境的传感器的新型信号处理算法。在这个二期项目中，信号处理算法将被广泛应用于新的动物植入物评估中。这项工作代表了对以往工作的重大创新，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. In Phase I, we developed novel signal processing algorithms suitable for sensors implanted in a complex tissue environment. In this Phase II project, 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.