[Retracted] EEG‐Based Epileptic Seizure Detection via Machine/Deep Learning Approaches: A Systematic Review
I Ahmad, X Wang, M Zhu, C Wang, Y Pi… - Computational …, 2022 - Wiley Online Library
Epileptic seizure is one of the most chronic neurological diseases that instantaneously
disrupts the lifestyle of affected individuals. Toward develo** novel and efficient …
disrupts the lifestyle of affected individuals. Toward develo** novel and efficient …
[HTML][HTML] Epileptic seizures detection using deep learning techniques: a review
A variety of screening approaches have been proposed to diagnose epileptic seizures,
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
using electroencephalography (EEG) and magnetic resonance imaging (MRI) modalities …
Wearable sensor‐based human activity recognition in the smart healthcare system
Human activity recognition (HAR) has been of interest in recent years due to the growing
demands in many areas. Applications of HAR include healthcare systems to monitor …
demands in many areas. Applications of HAR include healthcare systems to monitor …
A multivariate approach for patient-specific EEG seizure detection using empirical wavelet transform
Objective: This paper investigates the multivariate oscillatory nature of
electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure …
electroencephalogram (EEG) signals in adaptive frequency scales for epileptic seizure …
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 …
A new approach to characterize epileptic seizures using analytic time-frequency flexible wavelet transform and fractal dimension
The identification of seizure activities in non-stationary electroencephalography (EEG) is a
challenging task. The seizure detection by human inspection of EEG signals is prone to …
challenging task. The seizure detection by human inspection of EEG signals is prone to …
A multi-view deep learning framework for EEG seizure detection
The recent advances in pervasive sensing technologies have enabled us to monitor and
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …
analyze the multi-channel electroencephalogram (EEG) signals of epilepsy patients to …
Automated epileptic seizure detection using improved correlation-based feature selection with random forest classifier
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 …
EEG signal classification using universum support vector machine
Support vector machine (SVM) has been used widely for classification of
electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as …
electroencephalogram (EEG) signals for the diagnosis of neurological disorders such as …
A multi-class EEG-based BCI classification using multivariate empirical mode decomposition based filtering and Riemannian geometry
A brain-computer interface (BCI) facilitates a medium to translate the human motion
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …
intentions using electrical brain activity signals such as electroencephalogram (EEG) into …