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Related Concept Videos

Hypoglycemia and Glucagon01:15

Hypoglycemia and Glucagon

351
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...
351

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Related Experiment Video

Updated: Sep 17, 2025

Biochemical Measurement of Neonatal Hypoxia
13:13

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Published on: August 24, 2011

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Dual smart sensor data-based deep learning network for premature infant hypoglycemia detection.

Muhammad Shafiq1, J Kavitha2, Dhruva R Rinku3

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

Scientific Reports
|July 2, 2025
PubMed
Summary
This summary is machine-generated.

A new HAPI-BELT system uses dual sensors and Deep Learning to continuously track and detect hypoglycemia in preterm newborns, enabling urgent medical intervention. This technology monitors motion, skin color, and breathing for improved infant outcomes.

Keywords:
Biomedical signal processingEdge AIHypoglycemiaInternet of medical thingsRecurrent neural networksWearable health monitoring systems

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Area of Science:

  • Neonatal critical care
  • Biomedical engineering
  • Artificial intelligence in healthcare

Background:

  • Hypoglycemia is a frequent and disadvantageous pathology in preterm newborns.
  • Glucose is a critical energy substrate for fetal and neonatal development.
  • Metabolic changes at birth can lead to significant health challenges for neonates.

Purpose of the Study:

  • To introduce the HAPI-BELT system for continuous hypoglycemia detection in preterm infants.
  • To leverage dual intelligent sensors and Deep Learning algorithms for real-time monitoring.
  • To enable timely medical interventions and improve outcomes for vulnerable newborns.

Main Methods:

  • A smart belt equipped with an intelligent camera and photoplethysmography (PPG) sensor.
  • Tracking infant motion, skin color, breathing patterns, and heart function.
  • Utilizing Raspberry Pi Zero 2 W for data processing, CAT-Swarm Optimization for feature extraction, and a GRU-LSTM network for analysis.

Main Results:

  • The HAPI-BELT system successfully identifies infants with hypoglycemia (blood glucose < 70 mg/dL), pale complexion, and profuse perspiration.
  • Continuous monitoring and real-time data analysis enable prompt alerts to medical professionals.
  • The system demonstrates potential for early detection and urgent intervention.

Conclusions:

  • The HAPI-BELT system offers an integrated approach for real-time monitoring of preterm newborns.
  • Early detection of hypoglycemia through intelligent sensors and DL algorithms can significantly improve infant outcomes.
  • Urgent medical intervention facilitated by this technology is crucial for neonatal critical care.