Neurology

Epileptic Seizure Prediction Using Spatiotemporal Feature Fusion on EEG.

Electroencephalography (EEG) plays a crucial role in epilepsy analysis, and epileptic seizure prediction has significant value for clinical treatment of epilepsy. Currently, prediction methods using Convolutional Neural Network (CNN) primarily focus on local features of EEG, making it challenging to simultaneously capture the spatial and temporal features from multi-channel EEGs to identify the pr...

Bridging Imaging and Clinical Scores in Parkinson's Progression via Multimodal Self-Supervised Deep Learning.

Neurodegenerative diseases pose a formidable challenge to medical research, demanding a nuanced understanding of their progressive nature. In this regard, latent generative models can effectively be used in a data-driven modeling of different dimensions of neurodegeneration, framed within the context of the manifold hypothesis. This paper proposes a joint framework for a multi-modal, common latent...

A new machine learning model to predict the prognosis of cardiogenic brain infarction.

Cardiogenic cerebral infarction (CCI) is a disease in which the blood supply to the blood vessels in the brain is insufficient due to atherosclerosis ...

Deep learning-based auditory attention decoding in listeners with hearing impairment.

This study develops a deep learning (DL) method for fast auditory attention decoding (AAD) using electroencephalography (EEG) from listeners with hear...

A deep learning model for brain segmentation across pediatric and adult populations.

Automated quantification of brain tissues on MR images has greatly contributed to the diagnosis and follow-up of neurological pathologies across vario...

Evaluation of EEG Signals by Spectral Peak Methods and Statistical Correlation for Mental State Discrimination Induced by Arithmetic Tasks.

Bringing out brain activity through the interpretation of EEG signals is a challenging problem that involves combined methods of signal analysis. The ...

DMA-HPCNet: Dual Multi-Level Attention Hybrid Pyramid Convolution Neural Network for Alzheimer's Disease Classification.

Computer-aided diagnosis (CAD) plays a crucial role in the clinical application of Alzheimer's disease (AD). In particular, convolutional neural netwo...

Enhancing voxel-based dosimetry accuracy with an unsupervised deep learning approach for hybrid medical image registration.

BACKGROUND: Deformable registration is required to generate a time-integrated activity (TIA) map which is essential for voxel-based dosimetry. The con...

Lipoproteins and metabolites in diagnosing and predicting Alzheimer's disease using machine learning.

BACKGROUND: Alzheimer's disease (AD) is a chronic neurodegenerative disorder that poses a substantial economic burden. The Random forest algorithm is ...

Embedded Machine Learning System for Muscle Patterns Detection in a Patient with Shoulder Disarticulation.

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement i...

Explainability of three-dimensional convolutional neural networks for functional magnetic resonance imaging of Alzheimer's disease classification based on gradient-weighted class activation mapping.

Currently, numerous studies focus on employing fMRI-based deep neural networks to diagnose neurological disorders such as Alzheimer's Disease (AD), ye...

Robot-assisted gait training improves walking and cerebral connectivity in children with unilateral cerebral palsy.

BACKGROUND: Robot-assisted gait training (RAGT) is promising to help walking rehabilitation in cerebral palsy, but training-induced neuroplastic effec...

MRIO: the Magnetic Resonance Imaging Acquisition and Analysis Ontology.

Magnetic resonance imaging of the brain is a useful tool in both the clinic and research settings, aiding in the diagnosis and treatments of neurologi...

A novel feature extraction method PSS-CSP for binary motor imagery - based brain-computer interfaces.

In order to improve the performance of binary motor imagery (MI) - based brain-computer interfaces (BCIs) using electroencephalography (EEG), a novel ...

TASA: Temporal Attention With Spatial Autoencoder Network for Odor-Induced Emotion Classification Using EEG.

The olfactory system enables humans to smell different odors, which are closely related to emotions. The high temporal resolution and non-invasiveness...

A confounder controlled machine learning approach: Group analysis and classification of schizophrenia and Alzheimer's disease using resting-state functional network connectivity.

Resting-state functional magnetic resonance imaging (rs-fMRI) has increasingly been used to study both Alzheimer's disease (AD) and schizophrenia (SZ)...

Alzheimer's disease early screening and staged detection with plasma proteome using machine learning and convolutional neural network.

Alzheimer's disease (AD) stands as the prevalent progressive neurodegenerative disease, precipitating cognitive impairment and even memory loss. Amylo...

Deploying Robot-Led Activities for People with Dementia at Aged Care Facilities: A Feasibility Study.

OBJECTIVES: To explore the feasibility of deploying robot-led activities for people with dementia living in aged care facilities.

Parkinson's Disease Recognition Using Decorrelated Convolutional Neural Networks: Addressing Imbalance and Scanner Bias in rs-fMRI Data.

Parkinson's disease (PD) is a neurodegenerative and progressive disease that impacts the nerve cells in the brain and varies from person to person. Th...

The independence of impairments in proprioception and visuomotor adaptation after stroke.

BACKGROUND: Proprioceptive impairments are common after stroke and are associated with worse motor recovery and poor rehabilitation outcomes. Motor le...