AIMC Topic: Consciousness Disorders
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A Multimodal Consistency-Based Self-Supervised Contrastive Learning Framework for Automated Sleep Staging in Patients With Disorders of Consciousness.
IEEE journal of biomedical and health informatics
Feb 10, 2025
Sleep is a fundamental human activity, and automated sleep staging holds considerable investigational potential. Despite numerous deep learning methods proposed for sleep staging that exhibit notable performance, several challenges remain unresolved,...
Artificial intelligence and machine learning in disorders of consciousness.
Current opinion in neurology
Oct 9, 2024
PURPOSE OF REVIEW: As artificial intelligence and machine learning technologies continue to develop, they are being increasingly used to improve the scientific understanding and clinical care of patients with severe disorders of consciousness followi...
Predicting Long-Term Outcome of Prolonged Disorder of Consciousness in Children Through Machine Learning Based on Conventional Structural Magnetic Resonance Imaging.
Neurorehabilitation and neural repair
Sep 28, 2024
BACKGROUND: The prognosis of prolonged disorders of consciousness (pDoC) in children has consistently posed a formidable challenge in clinical decision-making.
Assessing Consciousness in Patients With Disorders of Consciousness Using a Musical Stimulation Paradigm and Verifiable Criteria.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Aug 20, 2024
Numerous studies have shown that musical stimulation can activate corresponding functional brain areas. Electroencephalogram (EEG) activity during musical stimulation can be used to assess the consciousness states of patients with disorders of consci...
Precise detection of awareness in disorders of consciousness using deep learning framework.
NeuroImage
Mar 18, 2024
Diagnosis of disorders of consciousness (DOC) remains a formidable challenge. Deep learning methods have been widely applied in general neurological and psychiatry disorders, while limited in DOC domain. Considering the successful use of resting-stat...
Constructing a Consciousness Meter Based on the Combination of Non-Linear Measurements and Genetic Algorithm-Based Support Vector Machine.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jan 8, 2020
OBJECTIVE: Constructing a framework to evaluate consciousness is an important issue in neuroscience research and clinical practice. However, there is still no systematic framework for quantifying altered consciousness along the dimensions of both lev...
Pharmacologically informed machine learning approach for identifying pathological states of unconsciousness via resting-state fMRI.
NeuroImage
Oct 29, 2019
Determining the level of consciousness in patients with disorders of consciousness (DOC) remains challenging. To address this challenge, resting-state fMRI (rs-fMRI) has been widely used for detecting the local, regional, and network activity differe...
Neural Networks, Computer
Machine Learning
Brain
Female
Aged
Young Adult
Adult
Male
Humans
Rest
Support Vector Machine
Unconsciousness
Magnetic Resonance Imaging
Persistent Vegetative State
Middle Aged
Anesthesia, General
Consciousness Disorders
Conscious Sedation
Functional Neuroimaging
Wakefulness
Deep Sedation
Adolescent
Child
Outcome prediction with serial neuron-specific enolase and machine learning in anoxic-ischaemic disorders of consciousness.
Computers in biology and medicine
Feb 16, 2019
BACKGROUND: The continuation of life-sustaining therapy in critical care patients with anoxic-ischemic disorders of consciousness (AI-DOC) depends on prognostic tests such as serum neuron-specific enolase (NSE) concentration levels.
Sleep in patients with disorders of consciousness characterized by means of machine learning.
PloS one
Jan 2, 2018
Sleep has been proposed to indicate preserved residual brain functioning in patients suffering from disorders of consciousness (DOC) after awakening from coma. However, a reliable characterization of sleep patterns in this clinical population continu...
Disentangling disorders of consciousness: Insights from diffusion tensor imaging and machine learning.
Human brain mapping
Sep 13, 2016
Previous studies have suggested that disorders of consciousness (DOC) after severe brain injury may result from disconnections of the thalamo-cortical system. However, thalamo-cortical connectivity differences between vegetative state (VS), minimally...
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