Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals
Background and objective Epilepsy is a prevalent disorder that affects the central nervous
system, causing seizures. In the current study, a novel algorithm is developed using …
system, causing seizures. In the current study, a novel algorithm is developed using …
Deep learning for patient-independent epileptic seizure prediction using scalp EEG signals
Epilepsy is one of the most prevalent neurological diseases among humans and can lead to
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
severe brain injuries, strokes, and brain tumors. Early detection of seizures can help to …
[HTML][HTML] One dimensional convolutional neural networks for seizure onset detection using long-term scalp and intracranial EEG
Epileptic seizure detection using scalp electroencephalogram (sEEG) and intracranial
electroencephalogram (iEEG) has attracted widespread attention in recent two decades …
electroencephalogram (iEEG) has attracted widespread attention in recent two decades …
Epileptic seizure detection by cascading isolation forest-based anomaly screening and EasyEnsemble
The electroencephalogram (EEG), for measuring the electrophysiological activity of the
brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG …
brain, has been widely applied in automatic detection of epilepsy seizures. Various EEG …
Automatic seizure detection by convolutional neural networks with computational complexity analysis
Abstract Background and Objectives Nowadays, an automated computer-aided diagnosis
(CAD) is an approach that plays an important role in the detection of health issues. The main …
(CAD) is an approach that plays an important role in the detection of health issues. The main …
Seizure onset detection using empirical mode decomposition and common spatial pattern
Automatic seizure onset detection plays an important role in epilepsy diagnosis. In this
paper, a novel seizure onset detection method is proposed by combining empirical mode …
paper, a novel seizure onset detection method is proposed by combining empirical mode …
Automatic epileptic seizure detection in EEG signals using sparse common spatial pattern and adaptive short-time Fourier transform-based synchrosqueezing …
Epilepsy can now be diagnosed more accurately and quickly due to computer-aided seizure
detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method …
detection utilizing Electroencephalography (EEG) recordings. In this work, a novel method …
An ensemble of hyperdimensional classifiers: Hardware-friendly short-latency seizure detection with automatic iEEG electrode selection
We propose a new algorithm for detecting epileptic seizures. Our algorithm first extracts
three features, namely mean amplitude, line length, and local binary patterns that are fed to …
three features, namely mean amplitude, line length, and local binary patterns that are fed to …
Neural memory networks for seizure type classification
D Ahmedt-Aristizabal, T Fernando… - 2020 42nd Annual …, 2020 - ieeexplore.ieee.org
Classification of seizure type is a key step in the clinical process for evaluating an individual
who presents with seizures. It determines the course of clinical diagnosis and treatment, and …
who presents with seizures. It determines the course of clinical diagnosis and treatment, and …
A spatiotemporal graph attention network based on synchronization for epileptic seizure prediction
Accurate early prediction of epileptic seizures can provide timely treatment for patients.
Previous studies have mainly focused on a single temporal or spatial dimension, making it …
Previous studies have mainly focused on a single temporal or spatial dimension, making it …