Latest AI and machine learning research in neurology for healthcare professionals.
During the course of a viral infection, virus-host protein-protein interactions (PPIs) play a critical role in allowing viruses to replicate and survive within the host. These interspecies molecular interactions can lead to viral-mediated perturbations of the human interactome causing the generation of various complex diseases. Evidences suggest that viral-mediated perturbations are a possible pat...
Erectile dysfunction (ED) remains a significant problem in up to 63% of men after robot-assisted radical prostatectomy (RARP). After the discovery of the neurovascular bundle (NVB), additional anatomic description and variation in nerve-sparing (NS) techniques have been described to improve post-RARP ED. However, it remains questionable whether ED rates have improved over time, and this is concern...
We propose an object recognition architecture relying on a neural network algorithm in optical sensors. Precisely, by applying the high-speed and low-...
Alzheimer's disease (AD) is a leading cause of dementia, and the current diagnostic methods of AD, such as positron emission tomography imaging, have ...
BACKGROUND: Dizziness is a common symptom in clinic, but there lacks an effective treatment method. This study sought to examine the efficiency of dee...
Electroencephalogram (EEG) signals have shown to be a good source of information for emotion recognition algorithms in Human-Brain interaction applica...
Parkinson's disease (PD) is a common neurodegenerative disease presenting with both motor and non-motor symptoms. Among PD motor symptoms, gait impair...
Traumatic brain injury (TBI) is a sudden injury that causes damage to the brain. TBI can have wide-ranging physical, psychological, and cognitive effe...
Machine learning methods, such as deep learning, show promising results in the medical domain. However, the lack of interpretability of these algorith...
In the past decade, the rapid development of machine learning has dramatically improved the performance of epileptic detection with Electroencephalogr...
Analysis and classification of electromyography (EMG) signals are crucial for rehabilitation and motor control. This study investigates electromyogram...
Tinnitus is attributed by the perception of a sound without any physical source causing the symptom. Symptom profiles of tinnitus patients are charact...
Machine learning and more recently deep learning have become valuable tools in clinical decision making for neonatal seizure detection. This work prop...
Schizophrenia is one of the most complex of all mental diseases. In this paper, we propose a symmetrically weighted local binary patterns (SLBP)-based...
The success of deep learning in computer vision has inspired the scientific community to explore new analysis methods. Within the field of neuroscienc...
Biomarkers are one of the primary medical signs to facilitate the early detection of Alzheimer's disease. The small beta-amyloid (Aβ) peptide is an im...
Convolutional neural networks (CNN) have been frequently used to extract subject-invariant features from electroencephalogram (EEG) for classification...
Human big data decoding is of great potential to reveal the complex patterns of human dynamics like physiological and biomechanical signals. In this s...
User authentication is an important security mechanism to prevent unauthorized accesses to systems or devices. In this paper, we propose a new user au...
The majority of studies for automatic epileptic seizure (ictal) detection are based on electroencephalogram (EEG) data, but electrocardiogram (ECG) pr...