AIMC Topic: Young Adult
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Dynamic network modeling and dimensionality reduction for human ECoG activity.
Journal of neural engineering
Aug 14, 2019
OBJECTIVE: Developing dynamic network models for multisite electrocorticogram (ECoG) activity can help study neural representations and design neurotechnologies in humans given the clinical promise of ECoG. However, dynamic network models have so far...
Physiological indices of challenge and threat: A data-driven investigation of autonomic nervous system reactivity during an active coping stressor task.
Psychophysiology
Aug 13, 2019
We utilized a data-driven, unsupervised machine learning approach to examine patterns of peripheral physiological responses during a motivated performance context across two large, independent data sets, each with multiple peripheral physiological me...
Discriminating schizophrenia using recurrent neural network applied on time courses of multi-site FMRI data.
EBioMedicine
Aug 13, 2019
BACKGROUND: Current fMRI-based classification approaches mostly use functional connectivity or spatial maps as input, instead of exploring the dynamic time courses directly, which does not leverage the full temporal information.
Neural-network classification of cardiac disease from P cardiovascular magnetic resonance spectroscopy measures of creatine kinase energy metabolism.
Journal of cardiovascular magnetic resonance : official journal of the Society for Cardiovascular Magnetic Resonance
Aug 12, 2019
BACKGROUND: The heart's energy demand per gram of tissue is the body's highest and creatine kinase (CK) metabolism, its primary energy reserve, is compromised in common heart diseases. Here, neural-network analysis is used to test whether noninvasive...
Heart Diseases
Energy Metabolism
Magnetic Resonance Spectroscopy
Phosphocreatine
Phosphorus Isotopes
Myocardium
Creatine Kinase
Kinetics
Adenosine Triphosphate
Reproducibility of Results
Aged
Aged, 80 and over
Young Adult
Adult
Male
Humans
Middle Aged
Neural Networks, Computer
Severity of Illness Index
Machine Learning
Predictive Value of Tests
Biomarkers
Female
Early prediction of epileptic seizures using a long-term recurrent convolutional network.
Journal of neuroscience methods
Aug 10, 2019
BACKGROUND: A seizure prediction system can detect seizures prior to their occurrence and allow clinicians to provide timely treatment for patients with epilepsy. Research on seizure prediction has progressed from signal processing analyses to machin...
Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study.
BMJ open
Aug 10, 2019
OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy.
Automation of the Timed-Up-and-Go Test Using a Conventional Video Camera.
IEEE journal of biomedical and health informatics
Aug 9, 2019
The Timed-Up-and-Go (TUG) test is a simple clinical tool commonly used to quickly assess the mobility of patients. Researchers have endeavored to automate the test using sensors or motion tracking systems to improve its accuracy and to extract more r...
Machine-Learning-Based Detection of Craving for Gaming Using Multimodal Physiological Signals: Validation of Test-Retest Reliability for Practical Use.
Sensors (Basel, Switzerland)
Aug 9, 2019
Internet gaming disorder in adolescents and young adults has become an increasing public concern because of its high prevalence rate and potential risk of alteration of brain functions and organizations. Cue exposure therapy is designed for reducing ...
A Real-Time Health 4.0 Framework with Novel Feature Extraction and Classification for Brain-Controlled IoT-Enabled Environments.
Neural computation
Aug 8, 2019
In this letter, we propose two novel methods for four-class motor imagery (MI) classification using electroencephalography (EEG). Also, we developed a real-time health 4.0 (H4.0) architecture for brain-controlled internet of things (IoT) enabled envi...
Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization.
Schizophrenia research
Aug 6, 2019
BACKGROUND: The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature vector, which destroys the 3D structure of MRI data. In order to both identify SCZ and find relevant b...
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