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Block based neural network for hypoglycemia detection

https://doi.org/10.1109/iembs.2011.6091371

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What this work says

Outgoing claims: the papers this work cites, builds on, or argues with.

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Genetic programming for classification and feature selection: analysis of1H nuclear magnetic resonance spectra from human brain tumour biopsies

inferredcites →https://doi.org/10.1007/978-3-662-02830-8openalex-snapshot

Genetic Algorithms + Data Structures = Evolution Programs

inferredcites →https://doi.org/10.1007/bf00994018openalex-snapshot

Support-Vector Networks

inferredcites →https://doi.org/10.1007/s10710-008-9076-8openalex-snapshot

Using enhanced genetic programming techniques for evolving classifiers in the context of medical diagnosis

inferredcites →https://doi.org/10.1016/j.eswa.2005.09.013openalex-snapshot

A support vector machine classifier algorithm based on a perturbation method and its application to ECG beat recognition systems

inferredcites →https://doi.org/10.1016/j.eswa.2008.06.127openalex-snapshot

Breast mass classification based on cytological patterns using RBFNN and SVM

inferredcites →https://doi.org/10.1016/s0925-2312(03)00431-4openalex-snapshot

The support vector machine under test

inferredcites →https://doi.org/10.1093/clinchem/39.9.1998openalex-snapshot

Overtraining in neural networks that interpret clinical data

inferredcites →https://doi.org/10.1109/2.485891openalex-snapshot

Artificial neural networks: a tutorial

inferredcites →https://doi.org/10.1109/5.4457openalex-snapshot

Self-processing networks and their biomedical implications

inferredcites →https://doi.org/10.1109/72.914525openalex-snapshot

Block-based neural networks

inferredcites →https://doi.org/10.1109/cec.2000.870279openalex-snapshot

Comparing inertia weights and constriction factors in particle swarm optimization

inferredcites →https://doi.org/10.1109/iembs.2006.259482openalex-snapshot

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

inferredcites →https://doi.org/10.1109/ijcnn.2002.1005610openalex-snapshot

Pattern recognition with block-based neural networks

inferredcites →https://doi.org/10.1109/tac.1997.633847openalex-snapshot

Neuro-Fuzzy and Soft Computing-A Computational Approach to Learning and Machine Intelligence [Book Review]

inferredcites →https://doi.org/10.1109/tbme.2008.918465openalex-snapshot

An ECG Signals Compression Method and Its Validation Using NNs

inferredcites →https://doi.org/10.1109/tnn.2007.900239openalex-snapshot

Block-Based Neural Networks for Personalized ECG Signal Classification

inferredcites →https://doi.org/10.1109/tsmcb.2008.921005openalex-snapshot

Hybrid Particle Swarm Optimization With Wavelet Mutation and Its Industrial Applications

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Statistics Notes: Diagnostic tests 1: sensitivity and specificity

What points at this work

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A review of modularization techniques in artificial neural networks

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Hand tremor-based hypoglycemia detection and prediction in adolescents with type 1 diabetes

inferred← citeshttps://doi.org/10.1016/j.neucom.2014.03.018openalex-snapshot

Hardware implementation of evolvable block-based neural networks utilizing a cost efficient sigmoid-like activation function

inferred← citeshttps://doi.org/10.1016/j.neucom.2014.05.033openalex-snapshot

Optimization of structure and system latency in evolvable block-based neural networks using genetic algorithm

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Evolvable Block-based Neural Networks for classification of driver drowsiness based on heart rate variability

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Evolvable Block-based Neural Networks for real-time classification of heart arrhythmia From ECG signals

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Co-simulation methodology for improved design and verification of hardware neural networks

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Industrial Application of Evolvable Block-Based Neural Network to Hypoglycemia Monitoring System

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A Novel Algorithm for Prediction and Detection of Hypoglycemia Based on Continuous Glucose Monitoring and Heart Rate Variability in Patients With Type 1 Diabetes

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Data-Driven Blood Glucose Pattern Classification and Anomalies Detection: Machine-Learning Applications in Type 1 Diabetes

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A Dynamically Reconfigurable BbNN Architecture for Scalable Neuroevolution in Hardware

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Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review

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Feature Selection for Blood Glucose Level Prediction in Type 1 Diabetes Mellitus by Using the Sequential Input Selection Algorithm (SISAL)

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Classification of Images Acquired with Colposcopy Using Artificial Neural Networks

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