Brain functional and effective connectivity based on electroencephalography recordings: A review
Functional connectivity and effective connectivity of the human brain, representing statistical
dependence and directed information flow between cortical regions, significantly contribute …
dependence and directed information flow between cortical regions, significantly contribute …
Epileptic seizure detection using machine learning: Taxonomy, opportunities, and challenges
Epilepsy is a life-threatening neurological brain disorder that gives rise to recurrent
unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many …
unprovoked seizures. It occurs due to abnormal chemical changes in our brains. For many …
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 …
Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals
Background and objective Epilepsy is a prevalent disorder that affects the central nervous
system, causing seizures. In the current study, a novel algorithm is developed using …
system, causing seizures. In the current study, a novel algorithm is developed using …
EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers
Epilepsy is a neurobiological disease caused by abnormal electrical activity of the human
brain. It is important to detect the epileptic seizures to help the epileptic patients. Using brain …
brain. It is important to detect the epileptic seizures to help the epileptic patients. Using brain …
An edge-fog computing-enabled lossless EEG data compression with epileptic seizure detection in IoMT networks
The need to improve smart health systems to monitor the health situation of patients has
grown as a result of the spread of epidemic diseases, the ageing of the population, the …
grown as a result of the spread of epidemic diseases, the ageing of the population, the …
An overview of machine learning methods in enabling IoMT-based epileptic seizure detection
ALN Al-Hajjar, AKM Al-Qurabat - The Journal of Supercomputing, 2023 - Springer
The healthcare industry is rapidly automating, in large part because of the Internet of Things
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
(IoT). The sector of the IoT devoted to medical research is sometimes called the Internet of …
Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers
Introduction In the clinical setting, it becomes increasingly important to detect epileptic
seizures automatically since it could significantly reduce the burden for the care of patients …
seizures automatically since it could significantly reduce the burden for the care of patients …
Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders
Deep-learning approaches with data augmentation have been widely used when
developing neuroimaging-based computer-aided diagnosis (CAD) systems. To prevent the …
developing neuroimaging-based computer-aided diagnosis (CAD) systems. To prevent the …
Machine learning algorithms for epilepsy detection based on published EEG databases: A systematic review
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …