Neurology

Novel Approaches to Detection of Cerebral Microbleeds: Single Deep Learning Model to Achieve a Balanced Performance.

PURPOSE: Cerebral microbleeds (CMBs) are considered essential indicators for the diagnosis of cerebrovascular disease and cognitive disorders. Traditionally, CMBs are manually interpreted based on criteria including the shape, diameter, and signal characteristics after an MR examination, such as susceptibility-weighted imaging or gradient echo imaging (GRE). In this paper, an efficient method for ...

A spectrogram image based intelligent technique for automatic detection of autism spectrum disorder from EEG.

Autism spectrum disorder (ASD) is a developmental disability characterized by persistent impairments in social interaction, speech and nonverbal communication, and restricted or repetitive behaviors. Currently Electroencephalography (EEG) is the most popular tool to inspect the existence of neurological disorders like autism biomarkers due to its low setup cost, high temporal resolution and wide a...

Universum based Lagrangian twin bounded support vector machine to classify EEG signals.

BACKGROUND AND OBJECTIVE: The detection of brain-related problems and neurological disorders like epilepsy, sleep disorder, and so on is done by using...

Improving Prehospital Stroke Diagnosis Using Natural Language Processing of Paramedic Reports.

BACKGROUND AND PURPOSE: Accurate prehospital diagnosis of stroke by emergency medical services (EMS) can increase treatments rates, mitigate disabilit...

Epileptic Seizure Detection on an Ultra-Low-Power Embedded RISC-V Processor Using a Convolutional Neural Network.

The treatment of refractory epilepsy via closed-loop implantable devices that act on seizures either by drug release or electrostimulation is a highly...

Semi-Automated Determination of Heavy Metals in Autopsy Tissue Using Robot-Assisted Sample Preparation and ICP-MS.

The endoprosthetic care of hip and knee joints introduces multiple materials into the human body. Metal containing implant surfaces release degradatio...

Automated Data Quality Control in FDOPA brain PET Imaging using Deep Learning.

INTRODUCTION: With biomedical imaging research increasingly using large datasets, it becomes critical to find operator-free methods to quality control...

Characterizing the Clinical Features and Atrophy Patterns of -Related Frontotemporal Dementia With Disease Progression Modeling.

BACKGROUND AND OBJECTIVE: Mutations in the gene cause frontotemporal dementia (FTD). Most previous studies investigating the neuroanatomical signatur...

Deep Learning-Based Stroke Disease Prediction System Using Real-Time Bio Signals.

The emergence of an aging society is inevitable due to the continued increases in life expectancy and decreases in birth rate. These social changes re...

Muscle network topology analysis for the classification of chronic neck pain based on EMG biomarkers extracted during walking.

Neuromuscular impairments are frequently observed in patients with chronic neck pain (CNP). This study uniquely investigates whether changes in neck m...

A Real-Time Clinical Decision Support System, for Mild Cognitive Impairment Detection, Based on a Hybrid Neural Architecture.

Clinical procedure for mild cognitive impairment (MCI) is mainly based on clinical records and short cognitive tests. However, low suspicion and diffi...

NDDRF: A risk factor knowledgebase for personalized prevention of neurodegenerative diseases.

INTRODUCTION: Neurodegenerative diseases (NDDs) are a series of chronic diseases, which are associated with progressive loss of neuronal structure or ...

Classification of Walking Environments Using Deep Learning Approach Based on Surface EMG Sensors Only.

Classification of terrain is a vital component in giving suitable control to a walking assistive device for the various walking conditions. Although s...

Exploring the Use of Brain-Computer Interfaces in Stroke Neurorehabilitation.

With the continuous development of artificial intelligence technology, "brain-computer interfaces" are gradually entering the field of medical rehabil...

Deep learning of resting-state electroencephalogram signals for three-class classification of Alzheimer's disease, mild cognitive impairment and healthy ageing.

This study aimed to produce a novel deep learning (DL) model for the classification of subjects with Alzheimer's disease (AD), mild cognitive impairme...

Machine learning models to select potential inhibitors of acetylcholinesterase activity from SistematX: a natural products database.

Alzheimer's disease is the most common form of dementia, representing 60-70% of dementia cases. The enzyme acetylcholinesterase (AChE) cleaves the est...

Robot-assisted arm therapy in neurological health conditions: rationale and methodology for the evidence synthesis in the CICERONE Italian Consensus Conference.

BACKGROUND: Robot-assisted Arm Therapy (RAT) has been increasingly applied in the last years for promoting functional recovery in patients with disabi...

Biomedical signals and machine learning in amyotrophic lateral sclerosis: a systematic review.

INTRODUCTION: The use of machine learning (ML) techniques in healthcare encompasses an emerging concept that envisages vast contributions to the tackl...

Experimental and numerical diagnosis of fatigue foot using convolutional neural network.

Fatigue is an essential criterion for physiotherapy in injured athletes. Muscle fatigue mechanism also is a crucial matter in designing a workout prog...

Use of artificial intelligence as an instrument of evaluation after stroke: a scoping review based on international classification of functioning, disability and health concept.

INTRODUCTION: To understand the current practices in stroke evaluation, the main clinical decision support system and artificial intelligence (AI) tec...