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 …
A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with …
This article presents a general methodology for processing non-stationary signals for the
purpose of classification and localization. The methodology combines methods adapted …
purpose of classification and localization. The methodology combines methods adapted …
Wavelet denoising based on the MAP estimation using the BKF prior with application to images and EEG signals
L Boubchir, B Boashash - IEEE Transactions on signal …, 2013 - ieeexplore.ieee.org
This paper presents a novel nonparametric Bayesian estimator for signal and image
denoising in the wavelet domain. This approach uses a prior model of the wavelet …
denoising in the wavelet domain. This approach uses a prior model of the wavelet …
Algorithm based on the short-term Rényi entropy and IF estimation for noisy EEG signals analysis
Stochastic electroencephalogram (EEG) signals are known to be nonstationary and often
multicomponential. Detecting and extracting their components may help clinicians to localize …
multicomponential. Detecting and extracting their components may help clinicians to localize …
Single channel EEG artifact identification using two-dimensional multi-resolution analysis
M Taherisadr, O Dehzangi, H Parsaei - Sensors, 2017 - mdpi.com
As a diagnostic monitoring approach, electroencephalogram (EEG) signals can be decoded
by signal processing methodologies for various health monitoring purposes. However, EEG …
by signal processing methodologies for various health monitoring purposes. However, EEG …
On the use of time-frequency features for detecting and classifying epileptic seizure activities in non-stationary EEG signals
L Boubchir, S Al-Maadeed… - 2014 IEEE International …, 2014 - ieeexplore.ieee.org
This paper proposes new time-frequency features for detecting and classifying epileptic
seizure activities in non-stationary EEG signals. These features are obtained by translating …
seizure activities in non-stationary EEG signals. These features are obtained by translating …
Haralick feature extraction from time-frequency images for epileptic seizure detection and classification of EEG data
L Boubchir, S Al-Maadeed… - 2014 26th International …, 2014 - ieeexplore.ieee.org
This paper presents novel time-frequency (tf) features based on tf image descriptors for the
automatic detection and classification of epileptic seizure activities in EEG data. Most …
automatic detection and classification of epileptic seizure activities in EEG data. Most …
Number of EEG signal components estimated using the short-term Rényi entropy
Multichannel electroencephalogram (EEG) signals are known to be highly non-stationary
and often multi-component. A new method for its complexity, in terms of number of signal …
and often multi-component. A new method for its complexity, in terms of number of signal …
Classification of EEG signals for detection of epileptic seizure activities based on LBP descriptor of time-frequency images
L Boubchir, S Al-Maadeed… - … Conference on Image …, 2015 - ieeexplore.ieee.org
This paper presents novel time-frequency (tf) feature extraction approach for the
classification of EEG signals for Epileptic seizure activities detection. The proposed features …
classification of EEG signals for Epileptic seizure activities detection. The proposed features …
On the selection of time-frequency features for improving the detection and classification of newborn EEG seizure signals and other abnormalities
B Boashash, L Boubchir - … , ICONIP 2012, Doha, Qatar, November 12-15 …, 2012 - Springer
This paper presents new time-frequency features for seizure detection in newborn EEG
signals. These features are obtained by translating some relevant time features or frequency …
signals. These features are obtained by translating some relevant time features or frequency …