Neurology

QuPWM: Feature Extraction Method for Epileptic Spike Classification.

Epilepsy is a neurological disorder ranked as the second most serious neurological disease known to humanity, after stroke. Inter-ictal spiking is an abnormal neuronal discharge after an epileptic seizure. This abnormal activity can originate from one or more cranial lobes, often travels from one lobe to another, and interferes with normal activity from the affected lobe. The common practice for I...

Letter to the Editor Re: "The Effects of a Robot-Assisted Arm Training Plus Hand Functional Electrical Stimulation on Recovery After Stroke: A Randomized Clinical Trial".

Motion Biomarkers Showing Maximum Contrast Between Healthy Subjects and Parkinson's Disease Patients Treated With Deep Brain Stimulation of the Subthalamic Nucleus. A Pilot Study.

Classic motion abnormalities in Parkinson's disease (PD), such as tremor, bradykinesia, or rigidity, are well-covered by standard clinical assessment...

Deep Learning Prediction of Mild Cognitive Impairment using Electronic Health Records.

About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting...

Somatosensory evoked fields predict response to vagus nerve stimulation.

There is an unmet need to develop robust predictive algorithms to preoperatively identify pediatric epilepsy patients who will respond to vagus nerve ...

Using artificial intelligence (AI) to predict postoperative surgical site infection: A retrospective cohort of 4046 posterior spinal fusions.

OBJECTIVES: Machine Learning and Artificial Intelligence (AI) are rapidly growing in capability and increasingly applied to model outcomes and complic...

Evaluation of machine learning methods to stroke outcome prediction using a nationwide disease registry.

INTRODUCTION: Being able to predict functional outcomes after a stroke is highly desirable for clinicians. This allows clinicians to set reasonable go...

Precision Psychiatry Applications with Pharmacogenomics: Artificial Intelligence and Machine Learning Approaches.

A growing body of evidence now suggests that precision psychiatry, an interdisciplinary field of psychiatry, precision medicine, and pharmacogenomics,...

A deep CNN approach to decode motor preparation of upper limbs from time-frequency maps of EEG signals at source level.

A system that can detect the intention to move and decode the planned movement could help all those subjects that can plan motion but are unable to im...

Selective peripheral nerve recordings from nerve cuff electrodes using convolutional neural networks.

OBJECTIVE: Recording and stimulating from the peripheral nervous system are becoming important components in a new generation of bioelectronics system...

A Sparse EEG-Informed fMRI Model for Hybrid EEG-fMRI Neurofeedback Prediction.

Measures of brain activity through functional magnetic resonance imaging (fMRI) or electroencephalography (EEG), two complementary modalities, are gro...

The feature extraction of resting-state EEG signal from amnestic mild cognitive impairment with type 2 diabetes mellitus based on feature-fusion multispectral image method.

Recently, combining feature extraction and classification method of electroencephalogram (EEG) signals has been widely used in identifying mild cognit...

A preliminary attempt to visualize nigrosome 1 in the substantia nigra for Parkinson's disease at 3T: An efficient susceptibility map-weighted imaging (SMWI) with quantitative susceptibility mapping using deep neural network (QSMnet).

PURPOSE: Visibility of nigrosome 1 in the substantia nigra (SN) is used as an MR imaging biomarker for Parkinson's disease. Because of lower susceptib...

Global channel attention networks for intracranial vessel segmentation.

Intracranial blood vessel segmentation plays an essential role in the diagnosis and surgical planning of cerebrovascular diseases. Recently, deep conv...

Estimation of absolute states of human skeletal muscle via standard B-mode ultrasound imaging and deep convolutional neural networks.

The objective is to test automated estimation of active and passive skeletal muscle states using ultrasonic imaging. Current technology (electromyogr...

Does robot-assisted gait training improve mobility, activities of daily living and quality of life in stroke? A single-blinded, randomized controlled trial.

The purpose of this study was to investigate the effects of robot-assisted gait training (RAGT) on mobility, activities of daily living (ADLs), and qu...

Automatic detection of rare pathologies in fundus photographs using few-shot learning.

In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, ...

Machine Learning Approach to Identify Stroke Within 4.5 Hours.

Background and Purpose- We aimed to investigate the ability of machine learning (ML) techniques analyzing diffusion-weighted imaging (DWI) and fluid-a...

Machine Learning for Detecting Early Infarction in Acute Stroke with Non-Contrast-enhanced CT.

Background Identifying the presence and extent of infarcted brain tissue at baseline plays a crucial role in the treatment of patients with acute isch...

Deep learning-based automated detection of glaucomatous optic neuropathy on color fundus photographs.

PURPOSE: To develop a deep learning approach based on deep residual neural network (ResNet101) for the automated detection of glaucomatous optic neuro...