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Levenberg-Marquardt-Based Non-Invasive Blood Glucose Measurement System

机译:基于Levenberg-Marquardt的无创血糖测量系统

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摘要

The present work focuses on the development of non-invasive blood glucose measurement device to revolutionize diabetes management and reduce severe complications associated with it. A low cost, painless and non-invasive blood glucose measurement system is designed using near-infrared (NIR) LED and four photodiodes for the purpose. NIR light emitted by LED passes through the skin and is detected by photodiodes after attenuation. The detector converts the attenuated light into a voltage signal. The interference due to background noise generated by human skin is removed by taking floating or internal reference. The voltage signal obtained from the photodiodes is calibrated using Levenberg-Marquardt-based Artificial Neural Network to obtain the glucose concentration. The accuracy of proposed prototype was examined by comparing non-invasively predicted data with invasively measured reference data. It is observed that all measurements lie in A and B zones of Clarke error grid and thus clinically accurate.
机译:目前的工作集中在无创血糖测量设备的开发上,以彻底改变糖尿病的治疗方法并减少与之相关的严重并发症。为此目的,使用近红外(NIR)LED和四个光电二极管设计了一种低成本,无痛且无创的血糖测量系统。 LED发射的近红外光穿过皮肤,衰减后被光电二极管检测到。检测器将衰减的光转换为电压信号。人体皮肤产生的背景噪声引起的干扰可以通过浮动参考或内部参考消除。使用基于Levenberg-Marquardt的人工神经网络校准从光电二极管获得的电压信号,以获得葡萄糖浓度。通过比较非侵入性预测数据与侵入性测量参考数据,检查了所提出原型的准确性。可以观察到,所有测量值都位于Clarke误差网格的A和B区域,因此临床上准确。

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