Neurology

Statistical algorithms for emotion classification via functional connectivity.

Pattern recognition algorithms decode emotional brain states by using functional connectivity measures which are extracted from EEG signals as input to the statistical classifiers. An open-access EEG dataset for emotional state analysis is used to classify two dominant emotional models, based on valence and arousal. To calculate the functional connectivity between all available pairs of EEG electr...

[Robotic-assisted supraomohyoid neck dissection via retroauricular hairline incision].

To explore the feasibility of using da Vinci Surgical System to perform supraomohyoid neck dissection (SOND) to avoid visible scar and reduce trauma. Between September 2017 and December 2018, twenty patients (two females and 18 males, mean age, 54.8 years) with oral cancer treated in the Department of Stomatology, Hainan Hospital of General Hospital of Chinese PLA were enrolled in this study. Ei...

Predictors of functional and motor outcomes following upper limb robot-assisted therapy after stroke.

Robot-assisted therapy is an effective treatment for stroke patients and has recently gained popularity. Clinicians and researchers are trying to iden...

Quantitative Analysis of Spinal Canal Areas in the Lumbar Spine: An Imaging Informatics and Machine Learning Study.

BACKGROUND AND PURPOSE: Quantitative imaging biomarkers have not been established for the diagnosis of spinal canal stenosis. This work aimed to lay t...

Percept-related EEG classification using machine learning approach and features of functional brain connectivity.

Machine learning is a promising approach for electroencephalographic (EEG) trials classification. Its efficiency is largely determined by the feature ...

Knee exoskeleton enhanced with artificial intelligence to provide assistance-as-needed.

Robotic therapy is a useful method applied during rehabilitation of stroke patients (to regain motor functions). To ensure active participation of the...

Epileptic seizure detection using EEG signals and extreme gradient boosting.

The problem of automated seizure detection is treated using clinical electroencephalograms (EEG) and machine learning algorithms on the Temple Univers...

Classification of low-density EEG for epileptic seizures by energy and fractal features based on EMD.

We are here to present a new method for the classification of epileptic seizures from electroencephalogram (EEG) signals. It consists of applying empi...

[Construction and analysis of muscle functional network for exoskeleton robot].

Exoskeleton nursing robot is a typical human-machine co-drive system. To full play the subjective control and action orientation of human, it is neces...

IDOMEN: An Extension of Infectious Disease Ontology for MENingitis.

In sub-Saharan African countries the prevention and control of epidemic diseases requires the improvement of the surveillance system for these disease...

Predicting task-general mind-wandering with EEG.

Mind-wandering refers to the process of thinking task-unrelated thoughts while performing a task. The dynamics of mind-wandering remain elusive becaus...

Using the structure of genome data in the design of deep neural networks for predicting amyotrophic lateral sclerosis from genotype.

MOTIVATION: Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease caused by aberrations in the genome. While several disease-causing vari...

An End-to-end System for Automatic Characterization of Iba1 Immunopositive Microglia in Whole Slide Imaging.

Traumatic brain injury (TBI) is one of the leading causes of death and disability worldwide. Detailed studies of the microglial response after TBI req...

Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering.

Amyotrophic lateral sclerosis (ALS) is a complex progressive neurodegenerative disorder with an estimated prevalence of about 5 per 100,000 people in ...

Development of Machine Learning Algorithms for Prediction of 30-Day Mortality After Surgery for Spinal Metastasis.

BACKGROUND: Preoperative prognostication of short-term postoperative mortality in patients with spinal metastatic disease can improve shared decision ...

Machine Learning in Modeling High School Sport Concussion Symptom Resolve.

INTRODUCTION: Concussion prevalence in sport is well recognized, so too is the challenge of clinical and return-to-play management for an injury with ...

An artificial neural network model for clinical score prediction in Alzheimer disease using structural neuroimaging measures.

BACKGROUND: The development of diagnostic and prognostic tools for Alzheimer disease is complicated by substantial clinical heterogeneity in prodromal...

Artificial Intelligence Based Hierarchical Clustering of Patient Types and Intervention Categories in Adult Spinal Deformity Surgery: Towards a New Classification Scheme that Predicts Quality and Value.

STUDY DESIGN: Retrospective review of prospectively-collected, multicenter adult spinal deformity (ASD) databases.

Evaluation of a Companion Robot for Individuals With Dementia: Quantitative Findings of the MARIO Project in an Irish Residential Care Setting.

The current study focuses on the short-term effect of MARIO, a social robot, on quality of life, depression, and perceived social support in persons w...

[Artificial Intelligence for Diagnostic Support of Neuroimage].

Artificial intelligence (AI) shows promises in terms of diagnostic support on neuroimaging. We developed a software that predicts Alzheimer's disease ...