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Your search keyword '"hypoglycemia detection"' showing total 5 results
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5 results on '"hypoglycemia detection"'

1. Blockchain associated machine learning and IoT based hypoglycemia detection system with auto-injection feature

Author

Rahnuma Mahzabin, Fahim Hossain Sifat, Sadia Anjum, Al-Akhir Nayan, and Muhammad Golam Kibria

Subjects
Signal Processing (eess.SP), FOS: Computer and information sciences, Internet of things, Computer Science - Machine Learning, Control and Optimization, Computer Networks and Communications, Computer Science - Artificial Intelligence, Machine Learning (cs.LG), Computer Science - Computers and Society, Blockchain, Artificial Intelligence (cs.AI), Hardware and Architecture, Hypoglycemia detection, Machine learning, Computers and Society (cs.CY), Signal Processing, FOS: Electrical engineering, electronic engineering, information engineering, Automatic injection, Electrical and Electronic Engineering, Electrical Engineering and Systems Science - Signal Processing, Information Systems
Abstract

Hypoglycemia is an unpleasant phenomenon caused by low blood glucose. The disease can lead a person to death or a high level of body damage. To avoid significant damage, patients need sugar. The research aims at implementing an automatic system to detect hypoglycemia and perform automatic sugar injections to save a life. Receiving the benefits of the internet of things (IoT), the sensor’s data was transferred using the hypertext transfer protocol (HTTP) protocol. To ensure the safety of health-related data, blockchain technology was utilized. The glucose sensor and smartwatch data were processed via Fog and sent to the cloud. A Random Forest algorithm was proposed and utilized to decide hypoglycemic events. When the hypoglycemic event was detected, the system sent a notification to the mobile application and auto-injection device to push the condensed sugar into the victim’s body. XGBoost, k-nearest neighbors (KNN), support vector machine (SVM), and decision tree were implemented to compare the proposed model's performance. The random forest performed 0.942 testing accuracy, better than other models in detecting hypoglycemic events. The system’s performance was measured in several conditions, and satisfactory results were achieved. The system can benefit hypoglycemia patients to survive this disease.

Published
2022

2. A particle swarm optimization-based neural network for detecting nocturnal hypoglycemia using electroencephalography signals.

Author

Nguyen, Lien B., Nguyen, Anh V., Ling, Sai Ho, and Nguyen, Hung T.

Abstract

For patients with Type 1 Diabetes Mellitus (T1DM), hypoglycemia or the state of low blood glucose level is a very common but dangerous complication. Hypoglycemia episodes can lead to a large number of serious symptoms and effects, including unconsciousness, coma and even death. The variety of hypoglycemia symptoms is originated from the inadequate supply of glucose to the brain. By analyzing electroencephalography (EEG) signals from five T1DM patients during an overnight study, we find that under hypoglycemia, both centroid theta frequency and centroid alpha frequency change significantly against non-hypoglycemia conditions. Furthermore, a neural network is developed to detect hypoglycemia using the mentioned two EEG features. A standard particle swarm optimization strategy is applied to optimize the parameters of this neural network. By using the proposed method, we obtain the classification performance of 82% sensitivity and 63% specificity. The results demonstrate that hypoglycemia episodes can be detected non-invasively and effectively from EEG signals. [ABSTRACT FROM PUBLISHER]

Published
2012
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3. Towards Wearable-based Hypoglycemia Detection and Warning in Diabetes

Author

Caterina Bérubé, Mathias Kraus, Thomas Züger, Tobias Kowatsch, Simon Föll, Christoph Stettler, Elgar Fleisch, Felix Wortmann, Vera Lehmann, Martin Maritsch, and Stefan Feuerriegel

Subjects
medicine.medical_specialty, Wearable computer, computer science, Hypoglycemia, Smartwatch, Diabetes mellitus, Medicine, Heart rate variability, Intensive care medicine, 610 Medicine & health, hypoglycemia detection, education, diabetes, explainable artificial intelligence, business.industry, information management, medicine.disease, Glucose management, False-positive result, wearables, machine learning, SHAP values, Glucose monitors, business, social sciences
Abstract

CHI EA '20: Extended Abstracts of the 2020 CHI Conference on Human Factors in Computing Systems, ISBN:978-1-4503-6819-3

Published
2020

4. Electrocardiographic Signals and Swarm-Based Support Vector Machine for Hypoglycemia Detection.

Author

Nuryani, Nuryani, Ling, Steve, and Nguyen, H.

Abstract

Cardiac arrhythmia relating to hypoglycemia is suggested as a cause of death in diabetic patients. This article introduces electrocardiographic (ECG) parameters for artificially induced hypoglycemia detection. In addition, a hybrid technique of swarm-based support vector machine (SVM) is introduced for hypoglycemia detection using the ECG parameters as inputs. In this technique, a particle swarm optimization (PSO) is proposed to optimize the SVM to detect hypoglycemia. In an experiment using medical data of patients with Type 1 diabetes, the introduced ECG parameters show significant contributions to the performance of the hypoglycemia detection and the proposed detection technique performs well in terms of sensitivity and specificity. [ABSTRACT FROM AUTHOR]

Published
2012
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5. ABSTRACT.

Subjects
*PEDIATRICS, *CONTINUUM of care, *VACCINATION of children, *PSYCHIATRIC drugs, *AUTISM in children
Abstract

The article presents abstracts on pediatric topics which includes continuity of care in infancy, vaccination of 14-year-old adolescents and administration of psychotropic drugs in children with autism spectrum disorders.

Published
2008

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