Data Analysis and Accuracy Evaluation of a Continuous Glucose-Monitoring Device

Cai, Liang; Wan-cheng, GE; Zhu, Zhigang; Zhao, Xueling; Li, Zhanhong

doi:10.1155/2019/4896862

Cited by 6 publications

(3 citation statements)

References 15 publications

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“…For continuous monitoring of glucose concentration in the blood, there is a 20-40 min delay [31]. In addition, taking sugar from food or injecting insulin requires a metabolic process that would take time.…”

Section: Resultsmentioning

confidence: 99%

Research of GPCEMBP Glucose Prediction Algorithm Based on Continuous Glucose Monitoring

Cai,

Ge,

Liu

2023

Journal of Sensors

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In view of the poor timeliness of dynamic blood glucose data and a delay of insulin effect for blood glucose control, and considering the nonlinearity and nonstationarity of the glucose data, a new blood Glucose Prediction algorithm combined Correlation coefficient-based complete ensemble empirical mode decomposition with adaptive noise and back propagation neural network (GPCEMBP) was proposed to increase the prediction time and improve the prediction accuracy. It refined the mode decomposition algorithm and integrated the correlation mode filter function to extract the characteristic intrinsic mode functions from the original signal. A new neural network prediction model was constructed by optimizing the number of hidden layer neurons, the number of hidden layers, activation functions, the number of inputs, structure, and other parameters. Finally, a predicted blood glucose value was calculated by phase space reconstruction technology. Through ablation and comparison experiments, it was demonstrated that the GPCEMBP algorithm had better prediction accuracy, convergence, and robustness in blood glucose prediction within 84 min. In addition, it has good adaptability to deal with different quality glucose data.

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Section: Resultsmentioning

confidence: 99%

Research of GPCEMBP Glucose Prediction Algorithm Based on Continuous Glucose Monitoring

Cai,

Ge,

Liu

2023

Journal of Sensors

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“…The flexible sensor subcutaneous sensor has a life span7-day with MARD 0f 10.22%. 94,95 Gradually, more companies have embarked on developing subcutaneous electrochemical CGM realizing their significance and importance. In 2019, Waveform Technology, formerly known as AgaMatrix, was granted CE approval to commercialize their cascade CGM system.…”

Section: Blood Glucose Vs Interstitial Glucosementioning

confidence: 99%

Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices

et al. 2022

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Diabetes leads to chronic microvascular complications for the heart, kidney, and eyes due to uncontrolled glycemic fluctuations. Self-monitoring blood glucose meters can only provide a snapshot of glucose level and are incapable of capturing the granular glucose fluctuations over a 24 hour period. Clinical research has indicated that random blood glucose fluctuations can lead to organ damage. In pursuit of better glucose management, continuous glucose monitoring (CGM) is emerging as a popular alternative owing to its ability to detect instantaneous changes in glucose levels and to alert the users of impending hypo- or hyper-glycemic events. In the last decade, several CGM devices have been launched in the market based on different glucose sensing chemistries and techniques. More research is still needed to come up with novel bio sensing concepts to make CGM low cost and highly accurate. Here, we elaborate the CGM techniques such as electrochemical, optical, reverse iontophoresis, microdialysis, and impedance spectroscopy. We emphasize on the widely used electrochemical CGMs with a focus on sensor design and bio-compatibility. We also provide an outlook for the future technologies, highlighting the need for innovative materials, possibility of integrating with the Internet of Things (IoT) for real-time e-health monitoring.

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“…In [34], AI-based classification techniques are used to process EEG data collected during the visual short-term memory. In [35], data analysis and accuracy evaluation are made using a continuous Glucose monitoring device. In [36], ECG based subject identification using statistical features and random forest is made.…”

mentioning

confidence: 99%

A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19

2020

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COVID-19, a deadly disease that originated in Wuhan, China, has resulted in a global outbreak. Patients infected with the causative virus SARS-CoV-2 are placed in quarantine, so the virus does not spread. The medical community has not discovered any vaccine that can be immediately used on patients infected with SARS-CoV-2. The only method discovered so far to protect people from this virus is keeping a distance from other people, wearing masks and gloves, as well as regularly washing and sanitizing hands. Government and law enforcement agencies are involved in banning the movement of people in different cities, to control the spread and monitor people following the guidelines of the CDC. But it is not possible for the government to monitor all places, such as shopping malls, hospitals, government offices, and banks, and guide people to follow the safety guidelines. In this paper, a novel technique is developed that can guide people to protect themselves from someone who has high exposure to the virus or has symptoms of COVID-19, such as having fever and coughing. Different deep Convolutional Neural Networks (CNN) models are implemented to test the proposed technique. The proposed intelligent monitoring system can be used as a complementary tool to be installed at different places and automatically monitor people adopting the safety guidelines. With these precautionary measurements, humans will be able to win this fight against COVID-19.

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Data Analysis and Accuracy Evaluation of a Continuous Glucose-Monitoring Device

Cited by 6 publications

References 15 publications

Research of GPCEMBP Glucose Prediction Algorithm Based on Continuous Glucose Monitoring

Research of GPCEMBP Glucose Prediction Algorithm Based on Continuous Glucose Monitoring

Review—Electrochemistry and Other Emerging Technologies for Continuous Glucose Monitoring Devices

A Novel Deep Convolutional Neural Network Model to Monitor People following Guidelines to Avoid COVID-19

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