A review of feature extraction for EEG epileptic seizure detection and classification
L Boubchir, B Daachi… - 2017 40th International …, 2017 - ieeexplore.ieee.org
Epileptic seizure is one of the most common neurological diseases around the world. It is
clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity …
clinical symptoms and/or signs due to abnormal excessive or synchronous neuronal activity …
A multi-view deep learning method for epileptic seizure detection using short-time fourier transform
With the advances in pervasive sensor technologies, physiological signals can be captured
continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic …
continuously to prevent the serious outcomes caused by epilepsy. Detection of epileptic …
Hybrid approach for classification of electroencephalographic signals using time–frequency images with wavelets and texture features
NJ Sairamya, L Susmitha, ST George… - Intelligent data analysis …, 2019 - Elsevier
Achieving the objective of identifying epileptic seizure activities automatically using
electroencephalographic (EEG) signals is of great significance in the treatment of epilepsy …
electroencephalographic (EEG) signals is of great significance in the treatment of epilepsy …
ROC analysis of EEG subbands for epileptic seizure detection using naïve bayes classifier
This paper presents analysis of Electroencephalograms (EEGs) and subbands (delta, theta,
alpha, beta, gamma) using image descriptors for epileptic seizure detection. Short-time …
alpha, beta, gamma) using image descriptors for epileptic seizure detection. Short-time …
Classification of colorectal cancer based on the association of multidimensional and multiresolution features
Colorectal cancer is one of the most common types of cancer according to worldwide
incidences statistics. The correct diagnosis of this lesion leads to the indication of the most …
incidences statistics. The correct diagnosis of this lesion leads to the indication of the most …
[HTML][HTML] Time–frequency texture descriptors of EEG signals for efficient detection of epileptic seizure
Detection of epileptic seizure in electroencephalogram (EEG) signals is a challenging task
and requires highly skilled neurophysiologists. Therefore, computer-aided detection helps …
and requires highly skilled neurophysiologists. Therefore, computer-aided detection helps …
ROC Analysis for detection of Epileptical Seizures using Haralick features of Gamma band
In this study, gamma band (30-60 Hz) is used for detection of epileptical seizures using
Haralick features. Most of the previous methods are based on the whole frequency spectrum …
Haralick features. Most of the previous methods are based on the whole frequency spectrum …
A new feature for the classification of non-stationary signals based on the direction of signal energy in the time–frequency domain
The detection of seizure activity in electroencephalogram (EEG) segments is very important
for the classification and localization of epileptic seizures. The evolution of a seizure in an …
for the classification and localization of epileptic seizures. The evolution of a seizure in an …
Performance evaluation of discrete wavelet transform, and wavelet packet decomposition for automated focal and generalized epileptic seizure detection
NJ Sairamya, MJ Premkumar, ST George… - IETE Journal of …, 2021 - Taylor & Francis
In the past decades, wavelet transforms are widely employed for characterizing the
electroencephalogram (EEG) signals for automatic diagnosis of epileptic seizure. But few …
electroencephalogram (EEG) signals for automatic diagnosis of epileptic seizure. But few …
Analysis of the influence of color normalization in the classification of non-hodgkin lymphoma images
In this work, a method is proposed to analyze the influence of color normalization in the
classification lymphoma images. The approach combines multidimensional fractal …
classification lymphoma images. The approach combines multidimensional fractal …