A review of feature extraction and performance evaluation in epileptic seizure detection using EEG
P Boonyakitanont, A Lek-Uthai, K Chomtho… - … Signal Processing and …, 2020 - Elsevier
Since the manual detection of electrographic seizures in continuous electroencephalogram
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
(EEG) monitoring is very time-consuming and requires a trained expert, attempts to develop …
A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals
Epilepsy, a brain disease generally associated with seizures, has tremendous effects on
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
people's quality of life. Diagnosis of epileptic seizures is commonly performed on …
Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier
M Mursalin, Y Zhang, Y Chen, NV Chawla - Neurocomputing, 2017 - Elsevier
Abstract Analysis of electroencephalogram (EEG) signal is crucial due to its non-stationary
characteristics, which could lead the way to proper detection method for the treatment of …
characteristics, which could lead the way to proper detection method for the treatment of …
[HTML][HTML] Tunable-Q wavelet transform based multiscale entropy measure for automated classification of epileptic EEG signals
This paper analyzes the underlying complexity and non-linearity of electroencephalogram
(EEG) signals by computing a novel multi-scale entropy measure for the classification of …
(EEG) signals by computing a novel multi-scale entropy measure for the classification of …
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 …
Automated diagnosis of epilepsy using key-point-based local binary pattern of EEG signals
The electroencephalogram (EEG) signals are commonly used for diagnosis of epilepsy. In
this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy …
this paper, we present a new methodology for EEG-based automated diagnosis of epilepsy …
Tetromino pattern based accurate EEG emotion classification model
Nowadays, emotion recognition using electroencephalogram (EEG) signals is becoming a
hot research topic. The aim of this paper is to classify emotions of EEG signals using a novel …
hot research topic. The aim of this paper is to classify emotions of EEG signals using a novel …
Epileptic seizure identification using entropy of FBSE based EEG rhythms
V Gupta, RB Pachori - Biomedical Signal Processing and Control, 2019 - Elsevier
This paper has proposed a new method for classification of epileptic seizures based on
weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier …
weighted multiscale Renyi permutation entropy (WMRPE) and rhythms obtained with Fourier …
Local pattern transformation based feature extraction techniques for classification of epileptic EEG signals
AK Jaiswal, H Banka - Biomedical Signal Processing and Control, 2017 - Elsevier
Background and objective According to the World Health Organization (WHO) epilepsy
affects approximately 45–50 million people. Electroencephalogram (EEG) records the …
affects approximately 45–50 million people. Electroencephalogram (EEG) records the …
Efficient biosignal processing using hyperdimensional computing: Network templates for combined learning and classification of ExG signals
Recognizing the very size of the brain's circuits, hyperdimensional (HD) computing can
model neural activity patterns with points in a HD space, that is, with HD vectors. Key …
model neural activity patterns with points in a HD space, that is, with HD vectors. Key …