Neurology

Subject-independent auditory spatial attention detection based on brain topology modeling and feature distribution alignment.

Auditory spatial attention detection (ASAD) seeks to determine which speaker in a surround sound field a listener is focusing on based on the one's brain biosignals. Although existing studies have achieved ASAD from a single-trial electroencephalogram (EEG), the huge inter-subject variability makes them generally perform poorly in cross-subject scenarios. Besides, most ASAD methods do not take ful...

Metabolic dysfunctions predict the development of Alzheimer's disease: Statistical and machine learning analysis of EMR data.

INTRODUCTION: The incidence of Alzheimer's disease (AD) and obesity rise concomitantly. This study examined whether factors affecting metabolism, race/ethnicity, and sex are associated with AD development.

Machine learning and wearable sensors for automated Parkinson's disease diagnosis aid: a systematic review.

BACKGROUND: The diagnosis of Parkinson's disease is currently based on clinical evaluation. Despite clinical hallmarks, unfortunately, the error rate ...

What the trained eye cannot see: Quantitative kinematics and machine learning detect movement deficits in early-stage Parkinson's disease from videos.

BACKGROUND: Evaluation of disease severity in Parkinson's disease (PD) relies on motor symptoms quantification. However, during early-stage PD, these ...

Automatic two-dimensional & three-dimensional video analysis with deep learning for movement disorders: A systematic review.

The advent of computer vision technology and increased usage of video cameras in clinical settings have facilitated advancements in movement disorder ...

Wasserstein generative adversarial network with gradient penalty and convolutional neural network based motor imagery EEG classification.

Due to the difficulty in acquiring motor imagery electroencephalography (MI-EEG) data and ensuring its quality, insufficient training data often leads...

Advancing aneurysm management: the potential of AI and machine learning in enhancing safety and predictive accuracy.

Cerebral aneurysm rupture, the predominant cause of non-traumatic subarachnoid hemorrhage, underscores the need for effective treatment and early dete...

The influence of mental calculations on brain regions and heart rates.

Performing mathematical calculations is a cognitive activity that can affect biological signals. This study aims to examine the changes in electroence...

Assessing the Effect of Cervical Transcutaneous Spinal Stimulation With an Upper Limb Robotic Exoskeleton and Surface Electromyography.

Transcutaneous spinal stimulation (TSS) is a promising rehabilitative intervention to restore motor function and coordination for individuals with spi...

Optimizing Acute Stroke Segmentation on MRI Using Deep Learning: Self-Configuring Neural Networks Provide High Performance Using Only DWI Sequences.

Segmentation of infarcts is clinically important in ischemic stroke management and prognostication. It is unclear what role the combination of DWI, AD...

Reproducibility and across-site transferability of an improved deep learning approach for aneurysm detection and segmentation in time-of-flight MR-angiograms.

This study aimed to (1) replicate a deep-learning-based model for cerebral aneurysm segmentation in TOF-MRAs, (2) improve the approach by testing vari...

Enhanced Hand Gesture Recognition with Surface Electromyogram and Machine Learning.

This study delves into decoding hand gestures using surface electromyography (EMG) signals collected via a precision Myo-armband sensor, leveraging ma...

Wavelet-based selection-and-recalibration network for Parkinson's disease screening in OCT images.

BACKGROUND AND OBJECTIVE: Parkinson's disease (PD) is one of the most prevalent neurodegenerative brain diseases worldwide. Therefore, accurate PD scr...

Feedback control of heart rate during robotics-assisted tilt table exercise in patients after stroke: a clinical feasibility study.

BACKGROUND: Patients with neurological disorders including stroke use rehabilitation to improve cognitive abilities, to regain motor function and to r...

Machine Learning and Deep Learning Approaches in Lifespan Brain Age Prediction: A Comprehensive Review.

The concept of 'brain age', derived from neuroimaging data, serves as a crucial biomarker reflecting cognitive vitality and neurodegenerative trajecto...

Cochlear Implant Artifacts Removal in EEG-Based Objective Auditory Rehabilitation Assessment.

Cochlear implant (CI) is a neural prosthesis that can restore hearing for patients with severe to profound hearing loss. Observed variability in audit...

Robustness of ML-Based Seizure Prediction Using Noisy EEG Data From Limited Channels.

Seizures pose a significant health hazard for over 50 million individuals with epilepsy worldwide, with approximately 56% experiencing uncontrollable ...

Incremental Value of Multidomain Risk Factors for Dementia Prediction: A Machine Learning Approach.

OBJECTIVE: The current evidence regarding how different predictor domains contributes to predicting incident dementia remains unclear. This study aims...

Joint use of population pharmacokinetics and machine learning for prediction of valproic acid plasma concentration in elderly epileptic patients.

BACKGROUND: Valproic acid (VPA) is a commonly used broad-spectrum antiepileptic drug. For elderly epileptic patients, VPA plasma concentrations have a...

Artificial intelligence-based classification of cardiac autonomic neuropathy from retinal fundus images in patients with diabetes: The Silesia Diabetes Heart Study.

BACKGROUND: Cardiac autonomic neuropathy (CAN) in diabetes mellitus (DM) is independently associated with cardiovascular (CV) events and CV death. Dia...