Neurology

Effect of Robot-Assisted Gait Training in a Large Population of Children With Motor Impairment Due to Cerebral Palsy or Acquired Brain Injury.

OBJECTIVE: To evaluate retrospectively the effect of robotic rehabilitation in a large group of children with motor impairment; an additional goal was to identify the effects in children with cerebral palsy (CP) and acquired brain injury (ABI) and with different levels of motor impairment according to the Gross Motor Function Classification System. Finally, we examined the effect of time elapsed f...

Seizure Prediction in Scalp EEG Using 3D Convolutional Neural Networks With an Image-Based Approach.

Epileptic seizures occur as a result of a process that develops over time and space in epileptic networks. In this study, we aim at developing a generalizable method for patient-specific seizure prediction by evaluating the spatio-temporal correlation in the features obtained from multichannel EEG signals. Spectral band power, statistical moment and Hjorth parameters are used to reveal the frequen...

Deep Learning and Glaucoma Specialists: The Relative Importance of Optic Disc Features to Predict Glaucoma Referral in Fundus Photographs.

PURPOSE: To develop and validate a deep learning (DL) algorithm that predicts referable glaucomatous optic neuropathy (GON) and optic nerve head (ONH)...

Machine Learning-Enabled Automated Determination of Acute Ischemic Core From Computed Tomography Angiography.

Background and Purpose- The availability of and expertise to interpret advanced neuroimaging recommended in the guideline-based endovascular stroke th...

Developing assistive robots for people with mild cognitive impairment and mild dementia: a qualitative study with older adults and experts in aged care.

OBJECTIVES: This research is part of an international project to design and test a home-based healthcare robot to help older adults with mild cognitiv...

EEG-based single-channel authentication systems with optimum electrode placement for different mental activities.

BACKGROUND: Electroencephalogram (EEG) signals of a brain contain a unique pattern for each person and the potential for biometric applications. Authe...

Robotic Exoskeleton for Wrist and Fingers Joint in Post-Stroke Neuro-Rehabilitation for Low-Resource Settings.

Robots have the potential to help provide exercise therapy in a repeatable and reproducible manner for stroke survivors. To facilitate rehabilitation ...

Using path signatures to predict a diagnosis of Alzheimer's disease.

The path signature is a means of feature generation that can encode nonlinear interactions in data in addition to the usual linear terms. It provides ...

EMG-based lumbosacral joint compression force prediction using a support vector machine.

Electromyography-assisted optimization (EMGAO) approach is widely used to predict lumbar joint loads under various dynamic and static conditions. Howe...

A zero-shot learning approach to the development of brain-computer interfaces for image retrieval.

Brain decoding-the process of inferring a person's momentary cognitive state from their brain activity-has enormous potential in the field of human-co...

Development of an unsupervised machine learning algorithm for the prognostication of walking ability in spinal cord injury patients.

BACKGROUND CONTEXT: Traumatic spinal cord injury can have a dramatic effect on a patient's life. The degree of neurologic recovery greatly influences ...

Technical considerations of multi-parametric tissue outcome prediction methods in acute ischemic stroke patients.

Decisions regarding acute stroke treatment rely heavily on imaging, but interpretation can be difficult for physicians. Machine learning methods can a...

Coupled artificial neural networks to estimate 3D whole-body posture, lumbosacral moments, and spinal loads during load-handling activities.

Biomechanical modeling approaches require body posture to evaluate the risk of spine injury during manual material handling. The procedure to measure ...

Deep Multi-View Feature Learning for EEG-Based Epileptic Seizure Detection.

Epilepsy is a neurological illness caused by abnormal discharge of brain neurons, where epileptic seizure can lead to life-threatening emergencies. By...

Quantifying brain metabolism from FDG-PET images into a probability of Alzheimer's dementia score.

F-fluorodeoxyglucose positron emission tomography (FDG-PET) enables in-vivo capture of the topographic metabolism patterns in the brain. These images...

Recognition of words from brain-generated signals of speech-impaired people: Application of autoencoders as a neural Turing machine controller in deep neural networks.

There is an essential requirement to support people with speech and communication disabilities. A brain-computer interface using electroencephalograph...

Methodological Advances in Leveraging Neuroimaging Datasets in Adolescent Substance Use Research.

PURPOSE OF REVIEW: Recent innovations in the statistical analysis of neuroimaging data related to adolescent substance use are highlighted. Going beyo...

A LightGBM-Based EEG Analysis Method for Driver Mental States Classification.

Fatigue driving can easily lead to road traffic accidents and bring great harm to individuals and families. Recently, electroencephalography- (EEG-) b...

External validation of the SORG 90-day and 1-year machine learning algorithms for survival in spinal metastatic disease.

BACKGROUND CONTEXT: Preoperative survival estimation in spinal metastatic disease helps determine the appropriateness of invasive management. The SORG...

Automatic detection of epileptic seizure based on approximate entropy, recurrence quantification analysis and convolutional neural networks.

Epilepsy is the most common neurological disorder in humans. Electroencephalogram is a prevalent tool for diagnosing the epileptic seizure activity in...