Neurology

Machine learning-assisted classification of hip conditions in pediatric cerebral palsy patients using migration percentage measurements.

Hip displacement is a significant concern in children with cerebral palsy (CP), necessitating accurate and timely assessment to prevent long-term complications. This study developed a support vector machine (SVM) model to classify hip conditions using migration percentage (MP) measurements obtained from 176 hips across 88 anteroposterior pelvic radiographs. MP values were categorized into three gr...

A Combined-Mode Machine Learning Model for Predicting Stroke Recurrence During Hospitalization in Patients with Acute Minor Ischemic Stroke.

Acute minor ischemic stroke patients often experience recurrence shortly after symptom onset, highlighting the importance of predicting stroke recurrence for guiding treatment decisions. This study evaluated the effectiveness of machine learning models in predicting in-hospital recurrence. The study cohort comprised 322,135 patients with acute minor ischemic stroke from 1439 centers, as establishe...

Altered cerebral functional activity and its associated genetic profiles underlying chronic insomnia disorder before and after treatment.

OBJECTIVES: The resting-state cerebral functional activity underlying chronic insomnia disorder (CID) remains inconsistent, and the effects of pharmac...

Exploring the diagnostic potential of EEG theta power and interhemispheric correlation of temporal lobe activities in Alzheimer's Disease through random forest analysis.

BACKGROUND: Considering the prevalence of Alzheimer's Disease (AD) among the aging population and the limited means of treatment, early detection emer...

Transcriptome Derived Artificial neural networks predict PRRC2A as a potent biomarker for epilepsy.

Epilepsy refers to the occurrence of two or more than two reiterative seizures. The occurrence of seizure is governed by the excessive electrical disc...

Artificial intelligence driven mental health diagnosis based on physiological signals.

Mental health disorders like stress, anxiety, and depression are increasing rapidly these days. Diagnosis of mental health disorders is a matter of co...

A GAN Guided Parallel CNN and Transformer Network for EEG Denoising.

Electroencephalography (EEG) signals are often contaminated with various physiological artifacts, seriously affecting the quality of subsequent analys...

Neural Manifold Decoder for Acupuncture Stimulations With Representation Learning: An Acupuncture-Brain Interface.

Acupuncture stimulations in somatosensory system can modulate spatiotemporal brain activity and improve cognitive functions of patients with neurologi...

CT-DCENet: Deep EEG Denoising via CNN-Transformer-Based Dual-Stage Collaborative Ensemble Learning.

Electroencephalogram (EEG) artifact removal has been investigated for decades with the goal of reconstructing the clean signals for the subsequent EEG...

Hierarchically Optimized Multiple Instance Learning With Multi-Magnification Pathological Images for Cerebral Tumor Diagnosis.

Accurate diagnosis of cerebral tumors is crucial for effective clinical therapeutics and prognosis. However, limitations in brain biopsy tissues and t...

Discovery of Shared Latent Nonlinear Effective Connectivity for EEG-Based Depression Detection.

Granger causality (GC) effective connectivity (EC) calculated from electroencephalogram (EEG) signals has been widely used in mental disorder detectio...

Fully Hyperbolic Neural Networks: A Novel Approach to Studying Aging Trajectories.

Characterizing age-related alterations in brain networks is crucial for understanding aging trajectories and identifying deviations indicative of neur...

Predicting 5-Year EDSS in Multiple Sclerosis with LSTM Networks: A Deep Learning Approach to Disease Progression.

BACKROUNDS: Multiple Sclerosis (MS) is a neurodegerative disease that is common worldwide, has no definitive cure yet, and negatively affects the indi...

EmT: A Novel Transformer for Generalized Cross-Subject EEG Emotion Recognition.

Integrating prior knowledge of neurophysiology into neural network architecture enhances the performance of emotion decoding. While numerous technique...

Advances in EEG-based detection of Major Depressive Disorder using shallow and deep learning techniques: A systematic review.

The contemporary diagnosis of Major Depressive Disorder (MDD) primarily relies on subjective assessments and self-reported measures, often resulting i...

Deep-ATM DL-LSTM: A novel adaptive thresholding model with dual-layer LSTM architecture for real-time driver drowsiness detection using skin conductance signals.

Driver drowsiness detection systems are crucial for road safety. However, existing machine learning models struggle to adjust thresholds for Skin Cond...

Unsupervised detection of sub-sequence anomalies in epilepsy EEG.

Seizures in electroencephalogram (EEG) data constitute a special case of sub-sequence anomalies in multivariate data with numerous challenges. These c...

Deep learning for multiple sclerosis lesion classification and stratification using MRI.

BACKGROUND AND OBJECTIVE: Multiple sclerosis (MS) is a chronic neurological disease characterized by inflammation, demyelination, and neurodegeneratio...

Development of a deep neural network model for ultra-early neurological deterioration in ischemic stroke and analysis of associated risk factors.

BACKGROUND: In this study, we established a deep neural network (DNN)-based predictive model, aiming to provide a basis for improving the treatment pr...

Spatio-temporal CNN-BiLSTM dynamic approach to emotion recognition based on EEG signal.

In this paper, a hybrid CNN-BiLSTM model for EEG-based emotion detection system is presented. The proposed technique is developed by extracting featur...