Neurology

Latest AI and machine learning research in neurology for healthcare professionals.

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Detection and location of EEG events using deep learning visual inspection.

The electroencephalogram (EEG) is a major diagnostic tool that provides detailed insight into the el...

A pilot study for speech assessment to detect the severity of Parkinson's disease: An ensemble approach.

BACKGROUND: Changes in voice are a symptom of Parkinson's disease and used to assess the progression...

Using clinical data to reclassify ESUS patients to large artery atherosclerotic or cardioembolic stroke mechanisms.

PURPOSE: Embolic stroke of unidentified source (ESUS) represents 10-25% of all ischemic strokes. Our...

Classifying Alzheimer's Disease Using a Finite Basis Physics Neural Network.

The disease amyloid plaques, neurofibrillary tangles, synaptic dysfunction, and neuronal death gradu...

DCSENets: Interpretable deep learning for patient-independent seizure classification using enhanced EEG-based spectrogram visualization.

Neurologists often face challenges in identifying epileptic activities within multichannel EEG recor...

Machine Learning Recognizes Stages of Parkinson's Disease Using Magnetic Resonance Imaging.

Neurodegenerative diseases (NDs), such as Alzheimer's disease (AD) and Parkinson's disease (PD), are...

Multivariate Modelling and Prediction of High-Frequency Sensor-Based Cerebral Physiologic Signals: Narrative Review of Machine Learning Methodologies.

Monitoring cerebral oxygenation and metabolism, using a combination of invasive and non-invasive sen...

Development of a Virtual Robot Rehabilitation Training System for Children with Cerebral Palsy: An Observational Study.

This paper presents the development of a robotic system for the rehabilitation and quality of life i...

Neuropathology of focal epilepsy: the promise of artificial intelligence and digital Neuropathology 3.0.

Focal lesions of the human neocortex often cause drug-resistant epilepsy, yet ​surgical resection of...

A U-Net based partial convolutional time-domain separation model to identify motor units from surface electromyographic signals in real time.

This study proposed a U-Net based partial convolutional time-domain model for a real-time high-densi...

Enhancing Deep-Learning Classification for Remote Motor Imagery Rehabilitation Using Multi-Subject Transfer Learning in IoT Environment.

One of the most promising applications for electroencephalogram (EEG)-based brain-computer interface...

Machine Learning and Statistical Analyses of Sensor Data Reveal Variability Between Repeated Trials in Parkinson's Disease Mobility Assessments.

Mobility tasks like the Timed Up and Go test (TUG), cognitive TUG (cogTUG), and walking with turns p...

Artificial intelligence tools for engagement prediction in neuromotor disorder patients during rehabilitation.

BACKGROUND: Robot-Assisted Gait Rehabilitation (RAGR) is an established clinical practice to encoura...

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