Neurology

Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus-transfer learning from existing algorithms.

BACKGROUND: For the segmentation of medical imaging data, a multitude of precise but very specific algorithms exist. In previous studies, we investigated the possibility of segmenting MRI data to determine cerebrospinal fluid and brain volume using a classical machine learning algorithm. It demonstrated good clinical usability and a very accurate correlation of the volumes to the single area deter...

Dynamical system based compact deep hybrid network for classification of Parkinson disease related EEG signals.

Electroencephalogram (EEG) signals accumulate the brain's spiking activities using standardized electrodes placed at the scalp. These cumulative brain signals are chaotic in nature and vary depending upon current physical and/or mental activities. The anatomy of the brain is altered when dopamine releasing neurons die because of Parkinson Disease (PD), a neurodegenerative disorder. The resulting a...

F-FDG PET-guided diffusion tractography reveals white matter abnormalities around the epileptic focus in medically refractory epilepsy: implications for epilepsy surgical evaluation.

BACKGROUND: Hybrid PET/MRI can non-invasively improve localization and delineation of the epileptic focus (EF) prior to surgical resection in medicall...

ResOT: Resource-Efficient Oblique Trees for Neural Signal Classification.

Classifiers that can be implemented on chip with minimal computational and memory resources are essential for edge computing in emerging applications ...

A machine learning approach to select features important to stroke prognosis.

Ischemic stroke is a common neurological disorder, and is still the principal cause of serious long-term disability in the world. Selection of feature...

Characterizing forearm muscle activity in young adults during dynamic wrist flexion-extension movement using a wrist robot.

Current research suggests that the wrist extensor muscles function as the primary stabilizers of the wrist-joint complex. However, most investigations...

Spatio-Temporal Representation of an Electoencephalogram for Emotion Recognition Using a Three-Dimensional Convolutional Neural Network.

Emotion recognition plays an important role in the field of human-computer interaction (HCI). An electroencephalogram (EEG) is widely used to estimate...

Effects of non-facilitated meaningful activities for people with dementia in long-term care facilities: A systematic review.

This systematic review sought to evaluate the effectiveness of non-facilitated meaningful activities for older people with dementia in long-term care ...

A Knowledge-Based Machine Learning Approach to Gene Prioritisation in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis is a neurodegenerative disease of the upper and lower motor neurons resulting in death from neuromuscular respiratory fa...

Machine learning and natural language processing methods to identify ischemic stroke, acuity and location from radiology reports.

Accurate, automated extraction of clinical stroke information from unstructured text has several important applications. ICD-9/10 codes can misclassif...

How Will Machine Learning Inform the Clinical Care of Atrial Fibrillation?

Machine learning applications in cardiology have rapidly evolved in the past decade. With the availability of machine learning tools coupled with vast...

The ReWalk ReStore™ soft robotic exosuit: a multi-site clinical trial of the safety, reliability, and feasibility of exosuit-augmented post-stroke gait rehabilitation.

BACKGROUND: Atypical walking in the months and years after stroke constrain community reintegration and reduce mobility, health, and quality of life. ...

A Deep Learning Model for Automated Sub-Basal Corneal Nerve Segmentation and Evaluation Using In Vivo Confocal Microscopy.

PURPOSE: The purpose of this study was to establish a deep learning model for automated sub-basal corneal nerve fiber (CNF) segmentation and evaluatio...

Insight into potent leads for alzheimer's disease by using several artificial intelligence algorithms.

Several proteins including S-nitrosoglutathione reductase (GSNOR), complement Factor D, complement 3b (C3b) and Protein Kinase R-like Endoplasmic Reti...

Stratifying patients using fast multiple kernel learning framework: case studies of Alzheimer's disease and cancers.

BACKGROUND: Predictive patient stratification is greatly emerging, because it allows us to prospectively identify which patients will benefit from wha...

Gait Event Detection for Stroke Patients during Robot-Assisted Gait Training.

Functional electrical stimulation and robot-assisted gait training are techniques which are used in a clinical routine to enhance the rehabilitation p...

Can robotic gait rehabilitation plus Virtual Reality affect cognitive and behavioural outcomes in patients with chronic stroke? A randomized controlled trial involving three different protocols.

BACKGROUND: The rehabilitation of cognitive and behavioral abnormalities in individuals with stroke is essential for promoting patient's recovery and ...

Characterizing forearm muscle activity in university-aged males during dynamic radial-ulnar deviation of the wrist using a wrist robot.

Functioning as wrist stabilizers, the wrist extensor muscles exhibit higher levels of muscle activity than the flexors in most distal upper-limb tasks...

Convolutional neural network for detection and classification of seizures in clinical data.

Epileptic seizure detection and classification in clinical electroencephalogram data still is a challenge, and only low sensitivity with a high rate o...

Auditory attention tracking states in a cocktail party environment can be decoded by deep convolutional neural networks.

OBJECTIVE: A deep convolutional neural network (CNN) is a method for deep learning (DL). It has a powerful ability to automatically extract features a...