Latest AI and machine learning research in seizures for healthcare professionals.
Although rehabilitation robotics seems to be a promising therapy in the rehabilitation of the upper limb in stroke patients, consensus is still lacking on its additive effects. Therefore, there is a need for determining the possible success of robotic interventions on selected patients, which in turn determine the necessity for new investigating instruments supporting the treatment decision-making...
Most simulations of cochlear implant (CI) coding strategies rely on standard vocoders that are based on purely signal processing techniques. However, these models neither account for various biophysical phenomena, such as neural stochasticity and refractoriness, nor for effects of electrical stimulation, such as spectral smearing as a function of stimulus intensity. In this paper, a neural model t...
Automatic classification of sleep stages is one of the most important methods used for diagnostic procedures in psychiatry and neurology. This method,...
OBJECTIVE Providing a reliable assessment of language lateralization is an important task to be performed prior to neurosurgery in patients with epile...
PURPOSE: Vitamin D status was evaluated in children with epilepsy taking anticonvulsants to determine the prevalence and risk factors of vitamin D def...
We report the case of a patient with fulminant myocarditis caused by influenza A virus, who presented with acute-onset heart failure and cardiogenic s...
Motor imagery electroencephalography is widely used in the brain-computer interface systems. Due to inherent characteristics of electroencephalography...
A simple and high throughput method was developed and validated for simultaneous determination of valproic acid and its two toxicant ene-metabolites, ...
UNLABELLED: This paper introduces a method utilizing spiking neural networks (SNN) for learning, classification, and comparative analysis of brain dat...
This paper proposes a generalized prediction system called a recurrent self-evolving fuzzy neural network (RSEFNN) that employs an on-line gradient de...
This study aims to secure medical data by combining them into one file format using steganographic methods. The electroencephalogram (EEG) is selected...
A growing body of literature suggests that changes in consciousness are reflected in specific connectivity patterns of the brain as obtained from rest...
We developed a machine learning methodology for automatic sleep stage scoring. Our time-frequency analysis-based feature extraction is fine-tuned to c...
In order to build a reliable index to monitor the depth of anesthesia (DOA), many algorithms have been proposed in recent years, one of which is sampl...
Automatic seizure detection has played an important role in the monitoring, diagnosis and treatment of epilepsy. In this paper, a patient specific met...
BACKGROUND AND PURPOSE: Novel approaches applying machine-learning methods to neuroimaging data seek to develop individualized measures that will aid ...
BACKGROUND AND PURPOSE: We analyzed the ability of a machine learning approach that uses diffusion tensor imaging (DTI) structural connectomes to dete...
BACKGROUND: Epilepsy is one of the most common neurological disorders approximately one in every 100 people worldwide are suffering from it. Uncontrol...
In this paper, a novel hierarchical multi-class SVM (H-MSVM) with extreme learning machine (ELM) as kernel is proposed to classify electroencephalogra...
Management of drug resistant focal epilepsy would be greatly assisted by a reliable warning system capable of alerting patients prior to seizures to a...