Neurology

Comprehensive Review: Machine and Deep Learning in Brain Stroke Diagnosis.

Brain stroke, or a cerebrovascular accident, is a devastating medical condition that disrupts the blood supply to the brain, depriving it of oxygen and nutrients. Each year, according to the World Health Organization, 15 million people worldwide experience a stroke. This results in approximately 5 million deaths and another 5 million individuals suffering permanent disabilities. The complex interp...

Predicting Alzheimer's disease from cognitive footprints in mid and late life: How much can register data and machine learning help?

BACKGROUND: Real-world data with decades-long medical records are increasingly available alongside the growing adoption of machine learning in healthcare research. We evaluated the performance of machine learning models in predicting the risk of Alzheimer's disease (AD) using data from the Finnish national registers.

EEG-based motor imagery channel selection and classification using hybrid optimization and two-tier deep learning.

Brain-computer interface (BCI) technology holds promise for individuals with profound motor impairments, offering the potential for communication and ...

Structure focused neurodegeneration convolutional neural network for modelling and classification of Alzheimer's disease.

Alzheimer's disease (AD), the predominant form of dementia, is a growing global challenge, emphasizing the urgent need for accurate and early diagnosi...

Deep learning-based stress detection for daily life use using single-channel EEG and GSR in a virtual reality interview paradigm.

This research aims to establish a practical stress detection framework by integrating physiological indicators and deep learning techniques. Utilizing...

Unraveling Brain Synchronisation Dynamics by Explainable Neural Networks using EEG Signals: Application to Dyslexia Diagnosis.

The electrical activity of the neural processes involved in cognitive functions is captured in EEG signals, allowing the exploration of the integratio...

A Method to Extract Task-Related EEG Feature Based on Lightweight Convolutional Neural Network.

Unlocking task-related EEG spectra is crucial for neuroscience. Traditional convolutional neural networks (CNNs) effectively extract these features bu...

Letter to the Editor: "The Combined Effect of Robot-assisted Therapy and Activities of Daily Living Training on Upper Limb Recovery in Persons With Subacute Stroke: A Randomized Controlled Trial".

A causal counterfactual graph neural network for arising-from-chair abnormality detection in parkinsonians.

The arising-from-chair task assessment is a key aspect of the evaluation of movement disorders in Parkinson's disease (PD). However, common scale-base...

Machine learning-based Cerebral Venous Thrombosis diagnosis with clinical data.

OBJECTIVES: Cerebral Venous Thrombosis (CVT) poses diagnostic challenges due to the variability in disease course and symptoms. The prognosis of CVT r...

A systematic literature review on the significance of deep learning and machine learning in predicting Alzheimer's disease.

BACKGROUND: Alzheimer's disease (AD) is the most prevalent cause of dementia, characterized by a steady decline in mental, behavioral, and social abil...

Advances in Computational Biology for Diagnosing Neurodegenerative Diseases: A Comprehensive Review.

The numerous and varied forms of neurodegenerative illnesses provide a considerable challenge to contemporary healthcare. The emergence of artificial ...

Power spectral density-based resting-state EEG classification of first-episode psychosis.

Historically, the analysis of stimulus-dependent time-frequency patterns has been the cornerstone of most electroencephalography (EEG) studies. The ab...

mmWave-RM: A Respiration Monitoring and Pattern Classification System Based on mmWave Radar.

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory ab...

Deep Learning-Enhanced Internet of Things for Activity Recognition in Post-Stroke Rehabilitation.

Wearable sensors provide a more effective means of activity monitoring and management by recording patients' daily activity data for assessing their d...

SMARTSeiz: Deep Learning With Attention Mechanism for Accurate Seizure Recognition in IoT Healthcare Devices.

The Internet of Things (IoT) is capable of controlling the healthcare monitoring system for remote-based patients. Epilepsy, a chronic brain syndrome ...

Predicting Alzheimer's Disease Progression Using a Versatile Sequence-Length-Adaptive Encoder-Decoder LSTM Architecture.

Detecting Alzheimer's disease (AD) accurately at an early stage is critical for planning and implementing disease-modifying treatments that can help p...

Hybrid Brain-Computer Interface Controlled Soft Robotic Glove for Stroke Rehabilitation.

Soft robotic glove controlled by a brain-computer interface (BCI) have demonstrated effectiveness in hand rehabilitation for stroke patients. Current ...

MASA-TCN: Multi-Anchor Space-Aware Temporal Convolutional Neural Networks for Continuous and Discrete EEG Emotion Recognition.

Emotion recognition from electroencephalogram (EEG) signals is a critical domain in biomedical research with applications ranging from mental disorder...

A Deep Learning Approach to Estimate Multi-Level Mental Stress From EEG Using Serious Games.

Stress is revealed by the inability of individuals to cope with their environment, which is frequently evidenced by a failure to achieve their full po...