Neurology

Stroke Prediction using Clinical and Social Features in Machine Learning

Every year in the United States, 800,000 individuals suffer a stroke - one person every 40 seconds, with a death occurring every four minutes. While individual factors vary, certain predictors are more prevalent in determining stroke risk. As strokes are the second leading cause of death and disability worldwide, predicting stroke likelihood based on lifestyle factors is crucial. Showing individ...

Leveraging Self-Training and Variational Autoencoder for Agitation Detection in People with Dementia Using Wearable Sensors

Dementia is a neurodegenerative disorder that has been growing among elder people over the past decades. This growth profoundly impacts the quality of life for patients and caregivers due to the symptoms arising from it. Agitation and aggression (AA) are some of the symptoms of people with severe dementia (PwD) in long-term care or hospitals. AA not only causes discomfort but also puts the patie...

Revealing the Self: Brainwave-Based Human Trait Identification

People exhibit unique emotional responses. In the same scenario, the emotional reactions of two individuals can be either similar or vastly differen...

Brain Ageing Prediction using Isolation Forest Technique and Residual Neural Network (ResNet)

Brain aging is a complex and dynamic process, leading to functional and structural changes in the brain. These changes could lead to the increased r...

[Three-dimensional convolutional neural network based on spatial-spectral feature pictures learning for decoding motor imagery electroencephalography signal].

The brain-computer interface (BCI) based on motor imagery electroencephalography (EEG) shows great potential in neurorehabilitation due to its non-inv...

[Image reconstruction for cerebral hemorrhage based on improved densely-connected fully convolutional neural network].

Cerebral hemorrhage is a serious cerebrovascular disease with high morbidity and high mortality, for which timely diagnosis and treatment are crucial....

RNN-Based Models for Predicting Seizure Onset in Epileptic Patients

Early management and better clinical outcomes for epileptic patients depend on seizure prediction. The accuracy and false alarm rates of existing sy...

A Review of Latent Representation Models in Neuroimaging

Neuroimaging data, particularly from techniques like MRI or PET, offer rich but complex information about brain structure and activity. To manage th...

Detection and Forecasting of Parkinson Disease Progression from Speech Signal Features Using MultiLayer Perceptron and LSTM

Accurate diagnosis of Parkinson disease, especially in its early stages, can be a challenging task. The application of machine learning techniques h...

Fatigue Monitoring Using Wearables and AI: Trends, Challenges, and Future Opportunities

Monitoring fatigue is essential for improving safety, particularly for people who work long shifts or in high-demand workplaces. The development of ...

Multi-atlas Ensemble Graph Neural Network Model For Major Depressive Disorder Detection Using Functional MRI Data

Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. I...

A Comparative Study on Machine Learning Models to Classify Diseases Based on Patient Behaviour and Habits

In recent years, ML algorithms have been shown to be useful for predicting diseases based on health data and posed a potential application area for ...

AlzheimerRAG: Multimodal Retrieval Augmented Generation for PubMed articles

Recent advancements in generative AI have flourished the development of highly adept Large Language Models (LLMs) that integrate diverse data types ...

Convolutional Deep Operator Networks for Learning Nonlinear Focused Ultrasound Wave Propagation in Heterogeneous Spinal Cord Anatomy

Focused ultrasound (FUS) therapy is a promising tool for optimally targeted treatment of spinal cord injuries (SCI), offering submillimeter precisio...

Robustness-enhanced Myoelectric Control with GAN-based Open-set Recognition

Electromyography (EMG) signals are widely used in human motion recognition and medical rehabilitation, yet their variability and susceptibility to n...

Linguistic Features Extracted by GPT-4 Improve Alzheimer's Disease Detection based on Spontaneous Speech

Alzheimer's Disease (AD) is a significant and growing public health concern. Investigating alterations in speech and language patterns offers a prom...

Utilizing Causal Network Markers to Identify Tipping Points ahead of Critical Transition

Early-warning signals of delicate design are always used to predict critical transitions in complex systems, which makes it possible to render the s...

LG-Sleep: Local and Global Temporal Dependencies for Mice Sleep Scoring

Efficiently identifying sleep stages is crucial for unraveling the intricacies of sleep in both preclinical and clinical research. The labor-intensi...

MUSTER: Longitudinal Deformable Registration by Composition of Consecutive Deformations

Longitudinal imaging allows for the study of structural changes over time. One approach to detecting such changes is by non-linear image registratio...

TuneS: Patient-specific model-based optimization of contact configuration in deep brain stimulation

Objective: The objective of this study is to develop and evaluate a systematic approach to optimize Deep Brain Stimulation (DBS) parameters, address...