This paper describes the analysis of a deep neural network for the classification of epileptic EEG signals. The deep learning architecture is made up of two convolutional layers for feature extraction and three fully-connected layers for classificati...
OBJECTIVE: This paper introduces a fully automated, subject-specific deep-learning convolutional neural network (CNN) system for forecasting seizures using ambulatory intracranial EEG (iEEG). The system was tested on a hand-held device (Mayo Epilepsy...
OBJECTIVE: Routinely collected healthcare data are a powerful research resource but often lack detailed disease-specific information that is collected in clinical free text, for example, clinic letters. We aim to use natural language processing techn...
In this paper the EEG signal is analyzed by reconstructing the time series EEG signal in High dimensional Phase Space. The computational complexity in higher dimension is reduced by Principal Component Analysis for the High dimensional Phase Space ou...
The application of deep learning to neuroimaging big data will help develop computer-aided diagnosis of neurological diseases. Pattern recognition using deep learning can extract features of neuroimaging signals unique to various neurological disease...
International journal of neural systems
Jan 8, 2019
Numerous nonepileptic paroxysmal events, such as syncope and psychogenic nonepileptic seizures, may imitate seizures and impede diagnosis. Misdiagnosis can lead to mistreatment, affecting patients' lives considerably. Electroencephalography is common...
Journal of chromatography. B, Analytical technologies in the biomedical and life sciences
Dec 14, 2018
Carbamazepine (CBZ) was considered as the drug of choice in the treatment for various forms of epilepsy, yet the non-negligible adverse effects of CBZ suspend as the major concern for rational and efficient clinical medication. This study developed a...
BMC medical informatics and decision making
Dec 7, 2018
BACKGROUND: Automated seizure detection from clinical EEG data can reduce the diagnosis time and facilitate targeting treatment for epileptic patients. However, current detection approaches mainly rely on limited features manually designed by domain ...
Several models have been proposed to explain brain regional and interregional communication, the majority of them using methods that tap the frequency domain, like spectral coherence. Considering brain interareal communication as binary interactions,...