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Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

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Without prolonged fasting, healthy individuals maintain blood glucose levels above 3.5 mM due to a well-adapted neuroendocrine counterregulatory system that effectively prevents acute hypoglycemia, a potentially life-threatening condition. The primary clinical scenarios for hypoglycemia encompass diabetes treatment, inappropriate production of endogenous insulin or insulin-like substances by tumors, and the use of glucose-lowering agents in non-diabetic individuals. Notably, hypoglycemia in the...
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相关实验视频

Updated: Sep 17, 2025

Biochemical Measurement of Neonatal Hypoxia
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基于数据的双智能传感器深度学习网络用于早产婴儿低血糖检测.

Muhammad Shafiq1, J Kavitha2, Dhruva R Rinku3

  • 1School of Information Engineering, Qujing Normal University, Yunnan, China.

Scientific reports
|July 2, 2025
PubMed
概括

一个新的HAPI-BELT系统使用双传感器和深度学习来持续跟踪和检测早产新生儿的低血糖症,从而实现紧急医疗干预. 这项技术监测运动,皮肤颜色和呼吸,以改善婴儿的结果.

关键词:
生物医学信号处理边缘AI 边缘AI低血糖症是一种低血糖症.互联网的医疗东西的互联网.经常性的神经网络.可穿戴式健康监测系统

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科学领域:

  • 新生儿重症监护服务新生儿重症监护服务
  • 生物医学工程 生物医学工程
  • 医疗保健中的人工智能

背景情况:

  • 低血糖是早产新生儿常见和不利的病理.
  • 葡萄糖是胎儿和新生儿发育的关键能量基质.
  • 出生时的新陈代谢变化可能会导致新生儿面临重大健康挑战.

研究的目的:

  • 引入HAPI-BELT系统,用于在早产婴儿中持续检测低血糖.
  • 利用双智能传感器和深度学习算法进行实时监控.
  • 为了实现及时的医疗干预,并改善弱势新生儿的结果.

主要方法:

  • 一个装有智能摄像头和光电显微镜 (PPG) 传感器的智能皮带.
  • 追踪婴儿的运动,皮肤颜色,呼吸模式和心脏功能.
  • 使用树派Zero 2W进行数据处理,CAT-Swarm优化进行特征提取,以及GRU-LSTM网络进行分析.

主要成果:

  • HAPI-BELT系统成功地识别了患有低血糖 (血糖<70 mg/dL),肤色白和大量出汗的婴儿.
  • 持续监控和实时数据分析使医疗专业人员能够迅速发出警报.
  • 该系统显示了早期检测和紧急干预的潜力.

结论:

  • HAPI-BELT系统提供了一种综合方法,用于实时监测早产新生儿.
  • 通过智能传感器和DL算法早期发现低血糖症可以显著改善婴儿的结果.
  • 这项技术所促进的紧急医疗干预对于新生儿重症监护至关重要.