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相关概念视频

Glucose Homeostasis: Regulation of Blood Glucose01:02

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Insulin is released by beta cells of the pancreas when blood glucose levels are high. It facilitates glucose absorption and utilization in insulin-dependent cells with insulin receptors on their plasma membranes. Insulin promotes glucose uptake by increasing the number of glucose transport proteins in the cell membrane, allowing glucose to enter the cell. As a result, glucose utilization and ATP production are enhanced.
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The Fourier series is instrumental in representing periodic functions, offering a powerful method to decompose such functions into a sum of sinusoids. This technique, however, necessitates modification when applied to nonperiodic functions. Consider a pulse-train waveform consisting of a series of rectangular pulses. When these pulses have a finite period, they can be accurately represented by a Fourier series. Yet, as the period approaches infinity, resulting in a single, isolated pulse, the...
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The Discrete-Time Fourier Transform (DTFT) is an essential mathematical tool for analyzing discrete-time signals, converting them from the time domain to the frequency domain. This transformation allows for examining the frequency components of discrete signals, providing insights into their spectral characteristics. In the DTFT, the continuous integral used in the continuous-time Fourier transform is replaced by a summation to accommodate the discrete nature of the signal.
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Using a Combination of Indirect Calorimetry, Infrared Thermography, and Blood Glucose Levels to Measure Brown Adipose Tissue Thermogenesis in Humans
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一个深度学习模型,它结合了ResNet和变压器架构,用于使用PPG信号实时测量血糖.

Ting-Hong Chen1, Lei Wang1, Qian-Xun Hong1

  • 1Department of Electrical Engineering, Feng-Chia University, Taichung 40724, Taiwan.

Bioengineering (Basel, Switzerland)
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PubMed
概括

这项研究通过开发使用生理信号的通用模型来增强糖尿病管理的非侵入性葡萄糖估计. 该模型表现出强的表现,缩小了个性化和主体独立预测之间的差距.

关键词:
克拉克错误网格 (CEG) 是一个错误网格.在MIMIC-III数据集中.物理网络 (PhysioNet) 是一个物理网络.电源网变压器混合动力模型信号质量指数 (SQI) 是指信号质量指数.血糖预测 血糖预测深度学习是一种深度学习.非侵袭性葡萄糖监测是一种非侵袭性的血糖监测.摄影复发性脑膜成像 (PPG)

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科学领域:

  • 生物医学工程 生物医学工程
  • 医疗信息学 医疗信息学
  • 数据科学数据科学数据科学

背景情况:

  • 非侵入性葡萄糖估计对于糖尿病管理至关重要,但目前的方法面临着个体变化和数据限制的挑战.
  • 现有的研究通常使用有限的受试者数据,并对相同的人进行训练/测试,可能会膨胀准确度指标.
  • 个体间的生理信号变化会影响葡萄糖估计模型的可靠性.

研究的目的:

  • 为了比较个性化的与非个性化的葡萄糖估计场景,以评估模型概括.
  • 开发和验证一个强大的,独立于主体的,非侵入性的葡萄糖估计模型.
  • 为了减少个性化和非个性化葡萄糖监测之间的性能差异.

主要方法:

  • 使用MIMIC-III数据集 (700,000个数据点,10,000名受试者) 进行模型培训和验证.
  • 采用ResNet CNN+变压器块架构进行生理信号分析.
  • 在预处理过程中实施数据质量分级,以增强信号选择和减少噪声.

主要成果:

  • 非个性化的模型在克拉克错误网格 (CEG) 的A区实现了15.16%的平均绝对相对差异 (MARD),在克拉克错误网格 (CEG) 的A区达到75.4%.
  • 个性化模型实现了11.69%的MARD,在CEG的A区达到82.7%,达到11.69%.
  • 预测总是在CEG A和B区内 (接近100%),这表明临床可接受性.

结论:

  • 拟议的方法表明了改善独立于受试者的非侵入性葡萄糖估计的潜力.
  • 个性化和非个性化模型之间的性能差距缩小了,这表明更好的概括.
  • 需要对不同种群进行进一步的验证,以确认该模型的广泛适用性.