Application of entropies for automated diagnosis of epilepsy using EEG signals: A review
Epilepsy is the neurological disorder of the brain which is difficult to diagnose visually using
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …
Electroencephalogram (EEG) signals. Hence, an automated detection of epilepsy using …
[HTML][HTML] Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis
Electroencephalography (EEG) is an important tool for studying the human brain activity and
epileptic processes in particular. EEG signals provide important information about …
epileptic processes in particular. EEG signals provide important information about …
[HTML][HTML] Machine-learning-based diagnostics of EEG pathology
LAW Gemein, RT Schirrmeister, P Chrabąszcz… - NeuroImage, 2020 - Elsevier
Abstract Machine learning (ML) methods have the potential to automate clinical EEG
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …
analysis. They can be categorized into feature-based (with handcrafted features), and end-to …
Automatic epileptic seizure detection in EEG signals using multi-domain feature extraction and nonlinear analysis
Epileptic seizure detection is commonly implemented by expert clinicians with visual
observation of electroencephalography (EEG) signals, which tends to be time consuming …
observation of electroencephalography (EEG) signals, which tends to be time consuming …
Classification of focal and non-focal EEG signals using neighborhood component analysis and machine learning algorithms
Background: Classification and localization of focal epileptic seizures provide a proper
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
diagnostic procedure for epilepsy patients. Visual identification of seizure activity from long …
Epileptic seizure classification of EEG time-series using rational discrete short-time Fourier transform
A system for epileptic seizure detection in electroencephalography (EEG) is described in this
paper. One of the challenges is to distinguish rhythmic discharges from nonstationary …
paper. One of the challenges is to distinguish rhythmic discharges from nonstationary …
Detection of epileptic electroencephalogram based on permutation entropy and support vector machines
N Nicolaou, J Georgiou - Expert Systems with Applications, 2012 - Elsevier
The electroencephalogram (EEG) has proven a valuable tool in the study and detection of
epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a …
epilepsy. This paper investigates for the first time the use of Permutation Entropy (PE) as a …
Classification of epileptic EEG recordings using signal transforms and convolutional neural networks
R San-Segundo, M Gil-Martín… - Computers in biology …, 2019 - Elsevier
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 …
EEG signals. The deep learning architecture is made up of two convolutional layers for …
1D-local binary pattern based feature extraction for classification of epileptic EEG signals
In this paper, an effective approach for the feature extraction of raw Electroencephalogram
(EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented …
(EEG) signals by means of one-dimensional local binary pattern (1D-LBP) was presented …
Epilepsy seizure detection using complete ensemble empirical mode decomposition with adaptive noise
Background: Epileptic seizure detection is traditionally performed by visual observation of
Electroencephalogram (EEG) signals. Owing to its onerous and time-consuming nature …
Electroencephalogram (EEG) signals. Owing to its onerous and time-consuming nature …