NDLI: A novel extreme learning machine for hypoglycemia detection

Content Provider	IEEE Xplore Digital Library
Author	Phyo Phyo San Sai Ho Ling Ni Ni Soe Nguyen, H.T.
Copyright Year	2014
Description	Author affiliation: Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore, Singapore (Ni Ni Soe) \|\| Centre for Health Technol., Univ. of Technol. Sydney, Sydney, NSW, Australia (Phyo Phyo San; Sai Ho Ling; Nguyen, H.T.)
Abstract	Hypoglycemia is a common side-effect of insulin therapy for patients with type 1 diabetes mellitus (T1DM) and is the major limiting factor to maintain tight glycemic control. The deficiency in glucose counter-regulation may even lead to severe hypoglycaemia. It is always threatening to the well-being of patients with T1DM since more severe hypoglycemia leads to seizures or loss of consciousness and the possible development of permanent brain dysfunction under certain circumstances. Thus, an accurate early detection on hypoglycemia is an important research topic. With the use of new emerging technology, an extreme learning machine (ELM) based hypoglycemia detection system is developed to recognize the presence of hypoglycemic episodes. From a clinical study of 16 children with T1DM, natural occurrence of nocturnal hypoglycemic episodes are associated with increased heart rates (p <; 0.06) and increased corrected QT intervals (p <; 0.001). The overall data were organized into a training set with 8 patients (320 data points) and a testing set with 8 patients (269 data points). By using the ELM trained feed-forward neural network (ELM-FFNN), the testing sensitivity (true positive) and specificity (true negative) for detection of hypoglycemia is 78 and 60% respectability.
Sponsorship	IEEE Eng. Med. Biol. Soc.
Starting Page	302
Ending Page	305
File Size	586325
Page Count	4
File Format	PDF
ISBN	9781424479290
ISSN	1557170X
DOI	10.1109/EMBC.2014.6943589
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-08-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Diabetes Heart rate Neural networks Training Sugar Testing Sensitivity
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Block based neural network for hypoglycemia detection

An adaptive strategy of classification for detecting hypoglycemia using only two EEG channels

Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor

Evolved fuzzy reasoning model for hypoglycaemic detection

Neural-Network Detection of Hypoglycemic Episodes in Children with Type 1 Diabetes using Physiological Parameters

Algorithm for Classifying Arrhythmia using Extreme Learning Machine and Principal Component Analysis

Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system

Detection of Hypoglycemic Episodes in Children with Type 1 Diabetes using an Optimal Bayesian Neural Network Algorithm

Glaucoma detection based on deep convolutional neural network

A novel extreme learning machine for hypoglycemia detection

Similar Documents

Block based neural network for hypoglycemia detection

An adaptive strategy of classification for detecting hypoglycemia using only two EEG channels

Real-time detection of nocturnal hypoglycemic episodes using a novel non-invasive hypoglycemia monitor

Evolved fuzzy reasoning model for hypoglycaemic detection

Neural-Network Detection of Hypoglycemic Episodes in Children with Type 1 Diabetes using Physiological Parameters

Algorithm for Classifying Arrhythmia using Extreme Learning Machine and Principal Component Analysis

Intelligent detection of hypoglycemic episodes in children with type 1 diabetes using adaptive neural-fuzzy inference system

Detection of Hypoglycemic Episodes in Children with Type 1 Diabetes using an Optimal Bayesian Neural Network Algorithm

Glaucoma detection based on deep convolutional neural network

A novel extreme learning machine for hypoglycemia detection