Neurology

Shared-task Self-supervised Learning for Estimating Free Movement Unified Parkinson's Disease Rating Scale III.

The Unified Parkinson's Disease Rating Scale (UP-DRS) is used to recognize patients with Parkinson's disease (PD) and rate its severity in clinical settings. Machine learning and wearables can reduce the need for clinical examinations and provide a reliable estimation of the severity of PD at home. This work introduces a multi-channel convolutional neural network to estimate UPDRS part III from mo...

Label Noise-Robust Ensemble Deep Multimodal Framework For Neuroimaging Data.

Neuroimaging data have become widely studied in the context of identifying brain-based markers of mental illness. however, this work is hampered by the use of symptom and self-report assessments of diagnosis, as well as lack of clarity in the nosological categories. Hence, treating existing diagnostic categories as label noise problems might be beneficial. Ensemble methods and deep learning models...

Multidimensional feature analysis shows stratification in robotic-motor-training gains based on the level of pre-training motor impairment in stroke.

Stroke involves heterogeneity in injury and ongoing endogenous recovery, which are seldom stratified before testing post-stroke robot assisted motor t...

Transformer-based Model Captures Neural Representation Differences between Nouns and Verbs in Spoken Narratives.

Nouns and verbs constitute the fundamental elements of human language systems. Abundant studies have demonstrated that nouns and verbs exhibit differe...

A Novel Wrist Device for Characterizing the Components of Proprioceptive Acuity.

Proprioception is important in human motor control but can be impaired by neurological disease. Unfortunately, our understanding of proprioceptive def...

Novel Alzheimer's Disease Stating Based on Comorbidities-Informed Graph Neural Networks.

Alzheimer's Disease (AD), the most prevalent form of dementia, requires early prediction for timely intervention. Leveraging data from the Alzheimer's...

Epileptic State Prediction using Phase Space Domain and Machine Learning Algorithms.

Epilepsy is a disease of the brain that causes unprovoked or reflex seizures that affects millions of individuals worldwide. Traditionally, identifyin...

Optimizing Coil Selection for Cerebral Aneurysm Treatment Using PyRadiomics and Machine Learning Models.

This study presents an innovative method to increase the accuracy of coil selection for treating cerebral aneurysms, leveraging advanced image analysi...

Knowledge-guided EEG Representation Learning.

Self-supervised learning has produced impressive results in multimedia domains of audio, vision and speech. This paradigm is equally, if not more, rel...

3D Multi-feature fusion convolutional network for Alzheimer's disease diagnosis.

The cognitive decline caused by Alzheimer's disease (AD) is closely related to the structural changes in the hippocampus captured by structural magnet...

An Explainable Transfer Learning Method for EEG-based Seizure Type Classification.

Epilepsy, traditionally conceptualized as a neurological disorder characterized by a persistent inclination toward epileptic seizures, is commonly dia...

Bi-hemisphere Interaction Convolutional Neural Network for Motor Imagery Classification.

Decoding EEG-based, Motor Imagery Brain-Computer Interfaces (MI-BCI) in a subject-independent manner is very challenging due to high dimensionality of...

Artificial Intelligence Based Hierarchical Classification of Frontotemporal Dementia.

Frontotemporal dementia (FTD) is a typical kind of presenile dementia with three main subtypes: behavioral-variant FTD (bvFTD), non-fluent variant pri...

Multi-class Prediction of Cognitively Normal / Mild Cognitive Impairment / Alzheimer's Disease Status in Dementia Based on Convolutional Neural Networks with Attention Mechanism.

Alzheimer's disease (AD) is a neurodegenerative disease with insidious onset and progressive development. AD is a health issue that is attracting atte...

Transformer-Based Wavelet-Scalogram Deep Learning for Improved Seizure Pattern Recognition in Post-Hypoxic-Ischemic Fetal Sheep EEG.

Hypoxic-ischemic (HI) events in newborns can trigger seizures, which are highly associated with later neurodevelopmental impairment. The precise detec...

Enhancing Myoelectric Prosthetic Control: Deep Learning Strategies for Continuous Arm Kinematics Estimation and Cross-Subject Model Transferability from EMG Data.

Over the last decade, myoelectric prosthesis control has witnessed considerable advancements, yet there remain significant challenges. Two key constra...

Enhancing Epileptic Seizure Detection with Random Input Selection in Graph-Wave Networks.

Graph neural networks show strong capability of learning spatial relationships between channels. In recent studies, they greatly advanced automatic ep...

LightIED: Explainable AI with Light CNN for Interictal Epileptiform Discharge Detection.

Interictal epileptic discharge (IED) detection from electroencephalography (EEG) is an important but difficult step in the epilepsy diagnosis. To redu...

Reconstruction of Continuous Hand Grasp Movement from EEG Using Deep Learning.

Brain-Computer Interface (BCI) is a promising neu-rotechnology offering non-muscular control of external devices, such as neuroprostheses and robotic ...

Video-based Clinical Gait Analysis in Parkinson's Disease: A Novel Approach Using Frontal Plane Videos and Machine Learning.

Gait can be significantly impaired by neurological conditions such as Parkinson's disease (PD). Gait impairments can be quantified by using instrument...