Neurology

Multimodal neuroimaging-based prediction of adult outcomes in childhood-onset ADHD using ensemble learning techniques.

Attention-deficit/hyperactivity disorder (ADHD) is a highly prevalent and heterogeneous neurodevelopmental disorder, which is diagnosed using subjective symptom reports. Machine learning classifiers have been utilized to assist in the development of neuroimaging-based biomarkers for objective diagnosis of ADHD. However, existing basic model-based studies in ADHD report suboptimal classification pe...

A review of methods for achieving upper limb movement following spinal cord injury through hybrid muscle stimulation and robotic assistance.

Individuals with tetraplegia, typically attributed to spinal cord injuries (SCI) at the cervical level, experience significant health care costs and loss of independence due to their limited reaching and grasping capabilities. Neuromuscular electrical stimulation (NMES) is a promising intervention to restore arm and hand function because it activates a person's own paralyzed muscles; however, NMES...

Latent Phase Identification of High-Frequency Micro-Scale Gamma Spike Transients in the Hypoxic Ischemic EEG of Preterm Fetal Sheep Using Spectral Analysis and Fuzzy Classifiers.

Premature babies are at high risk of serious neurodevelopmental disabilities, which in many cases are related to perinatal hypoxic-ischemic encephalop...

A machine learning-based linguistic battery for diagnosing mild cognitive impairment due to Alzheimer's disease.

There is a limited evaluation of an independent linguistic battery for early diagnosis of Mild Cognitive Impairment due to Alzheimer's disease (MCI-AD...

Machine learning-based motor assessment of Parkinson's disease using postural sway, gait and lifestyle features on crowdsourced smartphone data.

OBJECTIVES: Remote assessment of gait in patients' homes has become a valuable tool for monitoring the progression of Parkinson's disease (PD). Howeve...

SeizureBank: A Repository of Analysis-ready Seizure Signal Data.

Approximately 60 million people worldwide suffer from epileptic seizures. A key challenge in machine learning ap proaches for epilepsy research is the...

A Residual Based Attention Model for EEG Based Sleep Staging.

Sleep staging is to score the sleep state of a subject into different sleep stages such as Wake and Rapid Eye Movement (REM). It plays an indispensabl...

Hypofractionated Robotic Stereotactic Radiosurgery for Vagal Paragangliomas: A Novel Treatment Strategy for Cranial Nerve Preservation.

OBJECTIVE: To provide the first description of hypofractionated stereotactic radiosurgery (SRS) and evaluate tumor control and safety for vagal paraga...

Automated classification of five seizure onset patterns from intracranial electroencephalogram signals.

OBJECTIVE: The electroencephalographic (EEG) signals contain information about seizures and their onset location. There are several seizure onset patt...

CVSnet: A machine learning approach for automated central vein sign assessment in multiple sclerosis.

The central vein sign (CVS) is an efficient imaging biomarker for multiple sclerosis (MS) diagnosis, but its application in clinical routine is limite...

Constructing large-scale cortical brain networks from scalp EEG with Bayesian nonnegative matrix factorization.

A large-scale network provides a high hierarchical level for understanding the adaptive adjustment of the human brain during cognition processes. Sinc...

Novel Deep Learning Network Analysis of Electrical Stimulation Mapping-Driven Diffusion MRI Tractography to Improve Preoperative Evaluation of Pediatric Epilepsy.

OBJECTIVE: To investigate the clinical utility of deep convolutional neural network (DCNN) tract classification as a new imaging tool in the preoperat...

A-phase classification using convolutional neural networks.

A series of short events, called A-phases, can be observed in the human electroencephalogram (EEG) during Non-Rapid Eye Movement (NREM) sleep. These e...

The Effect of Using PARO for People Living With Dementia and Chronic Pain: A Pilot Randomized Controlled Trial.

OBJECTIVES: To evaluate the effect of interaction with a robotic seal (PARO) on pain and behavioral and psychological symptoms of people with dementia...

EMR-Based Phenotyping of Ischemic Stroke Using Supervised Machine Learning and Text Mining Techniques.

Ischemic stroke is a major cause of death and disability in adulthood worldwide. Because it has highly heterogeneous phenotypes, phenotyping of ischem...

A novel CNN based Alzheimer's disease classification using hybrid enhanced ICA segmented gray matter of MRI.

Predicting Alzheimer's Disease (AD) from Mild Cognitive Impairment (MCI) and Cognitive Normal (CN) has become wide. Recent advancement in neuroimaging...

Robot-assisted therapy for arm recovery for stroke patients: state of the art and clinical implication.

: Robot-assisted therapy is an emerging approach that performs highly repetitive, intensive, task oriented and quantifiable neuro-rehabilitation. In t...

Prediction of caregiver burden in amyotrophic lateral sclerosis: a machine learning approach using random forests applied to a cohort study.

OBJECTIVES: Amyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that is characterised by the rapid degeneration of upper and lower...

A randomized controlled study incorporating an electromechanical gait machine, the Hybrid Assistive Limb, in gait training of patients with severe limitations in walking in the subacute phase after stroke.

Early onset, intensive and repetitive, gait training may improve outcome after stroke but for patients with severe limitations in walking, rehabilitat...

Modeling of the neural mechanism underlying the terrestrial turning of the salamander.

In order to explore the neural mechanism underlying salamander terrestrial turning, an improved biomechanical model is proposed by modifying the forel...