Neurology

SpikeDeep-classifier: a deep-learning based fully automatic offline spike sorting algorithm.

Advancements in electrode design have resulted in micro-electrode arrays with hundreds of channels for single cell recordings. In the resulting electrophysiological recordings, each implanted electrode can record spike activity (SA) of one or more neurons along with background activity (BA). The aim of this study is to isolate SA of each neural source. This process is called spike sorting or spike...

Effect of assist-as-needed robotic gait training on the gait pattern post stroke: a randomized controlled trial.

BACKGROUND: Regaining gait capacity is an important rehabilitation goal post stroke. Compared to clinically available robotic gait trainers, robots with an assist-as-needed approach and multiple degrees of freedom (AAN) are expected to support motor learning, and might improve the post-stroke gait pattern. However, their benefits compared to conventional gait training have not yet been shown in a ...

Barriers and facilitators to the implementation of social robots for older adults and people with dementia: a scoping review protocol.

BACKGROUND: Psychosocial health issues such as depression and social isolation are an important cause of morbidity and premature mortality for older a...

Robotic lower extremity exoskeleton use in a non-ambulatory child with cerebral palsy: a case study.

PURPOSE: With few treatment options available for non-ambulatory children with cerebral palsy (CP), a robotic lower extremity gait trainer may provide...

Machine learning algorithms to predict seizure due to acute tramadol poisoning.

INTRODUCTION: This study was designed to develop and evaluate machine learning algorithms for predicting seizure due to acute tramadol poisoning, iden...

A Review of Robotic Interventional Neuroradiology.

Robotic interventional neuroradiology is an emerging field with the potential to enhance patient safety, reduce occupational hazards, and expand syste...

Deep Representation-Based Domain Adaptation for Nonstationary EEG Classification.

In the context of motor imagery, electroencephalography (EEG) data vary from subject to subject such that the performance of a classifier trained on d...

Machine Learning Models for Predicting Facial Nerve Palsy in Parotid Gland Surgery for Benign Tumors.

BACKGROUND: Despite the increasing use of intraoperative facial nerve monitoring during parotid gland surgery (PGS) and the improvement in the preoper...

Decision Tree Algorithm Identifies Stroke Patients Likely Discharge Home After Rehabilitation Using Functional and Environmental Predictors.

BACKGROUND AND PURPOSE: The importance of environmental factors for stroke patients to achieve home discharge was not scientifically proven. There are...

Cross-Domain Classification Model With Knowledge Utilization Maximization for Recognition of Epileptic EEG Signals.

Conventional classification models for epileptic EEG signal recognition need sufficient labeled samples as training dataset. In addition, when trainin...

Automatic rat brain image segmentation using triple cascaded convolutional neural networks in a clinical PET/MR.

The purpose of this work was to develop and evaluate a deep learning approach for automatic rat brain image segmentation of magnetic resonance imaging...

Spherical-Patches Extraction for Deep-Learning-Based Critical Points Detection in 3D Neuron Microscopy Images.

Digital reconstruction of neuronal structures is very important to neuroscience research. Many existing reconstruction algorithms require a set of goo...

Improved Segmentation of the Intracranial and Ventricular Volumes in Populations with Cerebrovascular Lesions and Atrophy Using 3D CNNs.

Successful segmentation of the total intracranial vault (ICV) and ventricles is of critical importance when studying neurodegeneration through neuroim...

Validation of a Machine Learning Brain Electrical Activity-Based Index to Aid in Diagnosing Concussion Among Athletes.

IMPORTANCE: An objective, reliable indicator of the presence and severity of concussive brain injury and of the readiness for the return to activity h...

Deep Learning for EEG Seizure Detection in Preterm Infants.

EEG is the gold standard for seizure detection in the newborn infant, but EEG interpretation in the preterm group is particularly challenging; trained...

The role of individual neuron ion conductances in the synchronization processes of neuron networks.

The partial phase synchronization (sometimes called cooperation) of neurons is fundamental for the understanding of the complex behavior of the brain....

Effects of wearable ankle robotics for stair and over-ground training on sub-acute stroke: a randomized controlled trial.

BACKGROUND: Wearable ankle robotics could potentially facilitate intensive repetitive task-specific gait training on stair environment for stroke reha...

A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer's disease.

Alzheimer's disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, res...

Deep Learning-Based Acute Ischemic Stroke Lesion Segmentation Method on Multimodal MR Images Using a Few Fully Labeled Subjects.

Acute ischemic stroke (AIS) has been a common threat to human health and may lead to severe outcomes without proper and prompt treatment. To precisely...

Evaluation for Parkinsonian Bradykinesia by deep learning modeling of kinematic parameters.

A wearable sensor system is available for monitoring of bradykinesia in patients with Parkinson's disease (PD), however, it remains unclear whether ki...