Neurology

Nonconvulsive epileptic seizure monitoring with incremental learning.

Nonconvulsive epileptic seizures (NCSz) and nonconvulsive status epilepticus (NCSE) are two neurological entities associated with increment in morbidity and mortality in critically ill patients. In a previous work, we introduced a method which accurately detected NCSz in EEG data (referred here as 'Batch method'). However, this approach was less effective when the EEG features identified at the be...

Comprehensive analysis of efficient swimming using articulated legs fringed with flexible appendages inspired by a water beetle.

Drag-based swimming is usually accompanied with the shape change of rowing appendages to generate asymmetric force during the power stroke and recovery stroke. To implement this in an aquatic robot, one may actively control the surface area of its legs during the swimming. However, a small sized robot with a limited number of actuators should adjust the surface area of legs in passive manner. For ...

World's fastest brain-computer interface: Combining EEG2Code with deep learning.

We present a novel approach based on deep learning for decoding sensory information from non-invasively recorded Electroencephalograms (EEG). It can e...

Uncovering convolutional neural network decisions for diagnosing multiple sclerosis on conventional MRI using layer-wise relevance propagation.

Machine learning-based imaging diagnostics has recently reached or even surpassed the level of clinical experts in several clinical domains. However, ...

Quantification of Motor Function in Huntington Disease Patients Using Wearable Sensor Devices.

Previous studies have demonstrated the feasibility and promise of wearable sensors as objective measures of motor impairment in Parkinson disease and ...

Multi-Atlas Segmentation of Anatomical Brain Structures Using Hierarchical Hypergraph Learning.

Accurate segmentation of anatomical brain structures is crucial for many neuroimaging applications, e.g., early brain development studies and the stud...

Two-step deep neural network for segmentation of deep white matter hyperintensities in migraineurs.

BACKGROUND AND OBJECTIVE: Patients with migraine show an increased presence of white matter hyperintensities (WMHs), especially deep WMHs. Segmentatio...

Bursts with High and Low Load of Epileptiform Spikes Show Context-Dependent Correlations in Epileptic Mice.

Hypersynchronous network activity is the defining hallmark of epilepsy and manifests in a wide spectrum of phenomena, of which electrographic activity...

The effect of using Gait Exercise Assist Robot (GEAR) on gait pattern in stroke patients: a cross-sectional pilot study.

: The Gait Exercise Assist Robot (GEAR) has been developed to support gait training for stroke patients. The GEAR can assist paretic lower limb swing ...

Deep regression neural networks for collateral imaging from dynamic susceptibility contrast-enhanced magnetic resonance perfusion in acute ischemic stroke.

PURPOSE: Acute ischemic stroke is one of the primary causes of death worldwide. Recent studies have shown that the assessment of collateral status cou...

Machine learning applications in epilepsy.

Machine learning leverages statistical and computer science principles to develop algorithms capable of improving performance through interpretation o...

Multi optimized SVM classifiers for motor imagery left and right hand movement identification.

EEG signal can be a good alternative for disabled persons who cannot perform actions or perform them improperly. Brain computer interface (BCI) is an ...

A Multi-Branch 3D Convolutional Neural Network for EEG-Based Motor Imagery Classification.

One of the challenges in motor imagery (MI) classification tasks is finding an easy-handled electroencephalogram (EEG) representation method which can...

Connectomic Profiling Identifies Responders to Vagus Nerve Stimulation.

OBJECTIVE: Vagus nerve stimulation (VNS) is a common treatment for medically intractable epilepsy, but response rates are highly variable, with no pre...

Active deep learning for the identification of concepts and relations in electroencephalography reports.

The identification of medical concepts, their attributes and the relations between concepts in a large corpus of Electroencephalography (EEG) reports ...

Development and validation of the automated imaging differentiation in parkinsonism (AID-P): a multicentre machine learning study.

BACKGROUND: Development of valid, non-invasive biomarkers for parkinsonian syndromes is crucially needed. We aimed to assess whether non-invasive diff...

Development and Validation of the Automated Imaging Differentiation in Parkinsonism (AID-P): A Multi-Site Machine Learning Study.

BACKGROUND: There is a critical need to develop valid, non-invasive biomarkers for Parkinsonian syndromes. The current 17-site, international study as...

Predicting PET-derived demyelination from multimodal MRI using sketcher-refiner adversarial training for multiple sclerosis.

Multiple sclerosis (MS) is the most common demyelinating disease. In MS, demyelination occurs in the white matter of the brain and in the spinal cord....

Investigation of bias in an epilepsy machine learning algorithm trained on physician notes.

Racial disparities in the utilization of epilepsy surgery are well documented, but it is unknown whether a natural language processing (NLP) algorithm...

The benefits of and barriers to using a social robot PARO in care settings: a scoping review.

BACKGROUND: Given the complexity of providing dementia care in hospitals, integrating technology into practice is a high challenge and an important op...