Neurology

Predicting individual decision-making responses based on single-trial EEG.

Decision-making plays an essential role in the interpersonal interactions and cognitive processing of individuals. There has been increasing interest in being able to predict an individual's decision-making response (i.e., acceptance or rejection). We proposed an electroencephalogram (EEG)-based computational intelligence framework to predict individual responses. Specifically, the discriminative ...

Machine Learning-Based Forecast of Hemorrhagic Stroke Healthcare Service Demand considering Air Pollution.

This study aimed to forecast the pattern of the demand for hemorrhagic stroke healthcare services based on air quality and machine learning. Hemorrhagic stroke, air quality, and meteorological data for 2016-2017 were obtained from the Longquanyi District of China, and the study included 1932 cases. Six machine learning methods were used to forecast the demand for hemorrhagic stroke healthcare serv...

Machine-learning-derived rules set excludes risk of Parkinson's disease in patients with olfactory or gustatory symptoms with high accuracy.

BACKGROUND: Chemosensory loss is a symptom of Parkinson's disease starting already at preclinical stages. Their appearance without an identifiable eti...

A distributed multitask multimodal approach for the prediction of Alzheimer's disease in a longitudinal study.

Predicting the progression of Alzheimer's Disease (AD) has been held back for decades due to the lack of sufficient longitudinal data required for the...

Trans-Differentiation of Human Dental Pulp Stem Cells Into Cholinergic-Like Neurons Via Nerve Growth Factor.

INTRODUCTION: Cell therapy has been widely considered as a therapeutic approach for neurodegenerative diseases and nervous system damage. Cholinergic ...

Temporally downsampled cerebral CT perfusion image restoration using deep residual learning.

PURPOSE: Acute ischemic stroke is one of the most causes of death all over the world. Onset to treatment time is critical in stroke diagnosis and trea...

Development of a deep learning model to identify hyperdense MCA sign in patients with acute ischemic stroke.

PURPOSE: The aim of this study was to develop an interactive deep learning-assisted identification of the hyperdense middle cerebral artery (MCA) sign...

Multi-indices quantification of optic nerve head in fundus image via multitask collaborative learning.

Multi-indices quantification of optic nerve head (ONH), measuring ONH appearance with multiple types of indices simultaneously from fundus images, is ...

Single-slice Alzheimer's disease classification and disease regional analysis with Supervised Switching Autoencoders.

BACKGROUND: Alzheimer's disease (AD) is a difficult to diagnose pathology of the brain that progressively impairs cognitive functions. Computer-assist...

A Multi-Column CNN Model for Emotion Recognition from EEG Signals.

We present a multi-column CNN-based model for emotion recognition from EEG signals. Recently, a deep neural network is widely employed for extracting ...

Comparison of Bagging and Boosting Ensemble Machine Learning Methods for Automated EMG Signal Classification.

The neuromuscular disorders are diagnosed using electromyographic (EMG) signals. Machine learning algorithms are employed as a decision support system...

Automated label-free detection of injured neuron with deep learning by two-photon microscopy.

Stroke is a significant cause of morbidity and long-term disability globally. Detection of injured neuron is a prerequisite for defining the degree of...

A comparison of machine learning classifiers for dementia with Lewy bodies using miRNA expression data.

BACKGROUND: Dementia with Lewy bodies (DLB) is the second most common subtype of neurodegenerative dementia in humans following Alzheimer's disease (A...

Hash Transformation and Machine Learning-Based Decision-Making Classifier Improved the Accuracy Rate of Automated Parkinson's Disease Screening.

Digitalized hand-drawn pattern is a noninvasive and reproducible assistive manner to obtain hand actions and motions for evaluating functional tremors...

A hierarchical sequential neural network with feature fusion for sleep staging based on EOG and RR signals.

OBJECTIVE: Currently, the automatic sleep staging methods mainly face two problems: the first problem is that although the algorithms which use electr...

Computational modeling of the effects of EEG volume conduction on functional connectivity metrics. Application to Alzheimer's disease continuum.

OBJECTIVE: The aim of this study was to evaluate the effect of electroencephalographic (EEG) volume conduction in different measures of functional con...

Incorporating feature selection methods into a machine learning-based neonatal seizure diagnosis.

The present study developed a feature selection (FS)-based decision support system using the electroencephalography (EEG) signals recorded from neonat...

Myoelectric Control of a Soft Hand Exoskeleton Using Kinematic Synergies.

Soft hand exoskeletons offer a lightweight, low-profile alternative to rigid rehabilitative robotic systems, enabling their use to restore activities ...

Detection of Participation and Training Task Difficulty Applied to the Multi-Sensor Systems of Rehabilitation Robots.

In the process of rehabilitation training for stroke patients, the rehabilitation effect is positively affected by how much physical activity the pati...

Integrated robotics platform with haptic control differentiates subjects with Parkinson's disease from controls and quantifies the motor effects of levodopa.

BACKGROUND: The use of integrated robotic technology to quantify the spectrum of motor symptoms of Parkinson's Disease (PD) has the potential to facil...