Neurology

A Generative Model to Synthesize EEG Data for Epileptic Seizure Prediction.

OBJECTIVE: Scarcity of good quality electroencephalography (EEG) data is one of the roadblocks for accurate seizure prediction. This work proposes a deep convolutional generative adversarial network (DCGAN) to generate synthetic EEG data. Another objective of our study is to use transfer-learning (TL) for evaluating the performance of four well-known deep-learning (DL) models to predict epileptic ...

Computer-Aided Diagnosis of Children with Cerebral Palsy under Deep Learning Convolutional Neural Network Image Segmentation Model Combined with Three-Dimensional Cranial Magnetic Resonance Imaging.

In this paper, we analyzed the application value and effect of deep learn-based image segmentation model of convolutional neural network (CNN) algorithm combined with 3D brain magnetic resonance imaging (MRI) in diagnosis of cerebral palsy in children. 3D brain model was segmented based on CNN algorithm to obtain the segmented MRI images of brain tissue, and the validity was verified. Then, 70 chi...

Ambulatory seizure forecasting with a wrist-worn device using long-short term memory deep learning.

The ability to forecast seizures minutes to hours in advance of an event has been verified using invasive EEG devices, but has not been previously dem...

Synergy-Based Neural Interface for Human Gait Tracking With Deep Learning.

Neural information decomposed from electromyography (EMG) signals provides a new path of EMG-based human-machine interface. Instead of the motor unit ...

Phybrata Sensors and Machine Learning for Enhanced Neurophysiological Diagnosis and Treatment.

Concussion injuries remain a significant public health challenge. A significant unmet clinical need remains for tools that allow related physiological...

Comparison of Morphine and Dexmedetomidine as Adjuvants to Isobaric Levobupivacaine for Spinal Anesthesia in Patients Undergoing Abdominal Hysterectomy.

INTRODUCTION: Various adjuvants to local anesthetics are used in spinal anesthesia for improving the quality and prolonging postoperative analgesia. W...

Coherent false seizure prediction in epilepsy, coincidence or providence?

OBJECTIVE: Seizure forecasting using machine learning is possible, but the performance is far from ideal, as indicated by many false predictions and l...

A Novel Context Aware Joint Segmentation and Classification Framework for Glaucoma Detection.

Glaucoma is a chronic ocular disease characterized by damage to the optic nerve resulting in progressive and irreversible visual loss. Early detection...

EEG-based detection of emotional valence towards a reproducible measurement of emotions.

A methodological contribution to a reproducible Measurement of Emotions for an EEG-based system is proposed. Emotional Valence detection is the sugges...

Beneficial Effects of Robot-Assisted Gait Training on Functional Recovery in Women after Stroke: A Cohort Study.

Robot-assisted gait training (RAGT) could be a rehabilitation option for patients after experiencing a stroke. This study aims to determine the sex-r...

Machine Learning for Predicting Motor Improvement After Acute Subcortical Infarction Using Baseline Whole Brain Volumes.

Neuroimaging biomarkers are valuable predictors of motor improvement after stroke, but there is a gap between published evidence and clinical usage. ...

A nested parallel multiscale convolution for cerebrovascular segmentation.

PURPOSE: Cerebrovascular segmentation in magnetic resonance imaging (MRI) plays an important role in the diagnosis and treatment of cerebrovascular di...

Robot-assisted stereoelectroencephalography in young children: technical challenges and considerations.

Robot-assisted stereoelectroencephalography (sEEG) is frequently employed to localize epileptogenic zones in patients with medically refractory epilep...

3D hemisphere-based convolutional neural network for whole-brain MRI segmentation.

Whole-brain segmentation is a crucial pre-processing step for many neuroimaging analyses pipelines. Accurate and efficient whole-brain segmentations a...

Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm.

OBJECTIVE: To assess a new deep learning-based MR reconstruction method, "DLRecon," for clinical evaluation of peripheral nerves.

Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease.

Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI)...

Deep learning for neuroimaging-based diagnosis and rehabilitation of Autism Spectrum Disorder: A review.

Accurate diagnosis of Autism Spectrum Disorder (ASD) followed by effective rehabilitation is essential for the management of this disorder. Artificial...

Prediction of genetic alteration of phospholipase C isozymes in brain disorders: Studies with deep learning.

Genetic mutations leading to the development of various diseases, such as cancer, diabetes, and neurodegenerative disorders, can be attributed to mult...

Using deep learning to classify pediatric posttraumatic stress disorder at the individual level.

BACKGROUND: Children exposed to natural disasters are vulnerable to developing posttraumatic stress disorder (PTSD). Previous studies using resting-st...

White matter hyperintensities segmentation using an ensemble of neural networks.

White matter hyperintensities (WMHs) represent the most common neuroimaging marker of cerebral small vessel disease (CSVD). The volume and location of...