Brain functional and effective connectivity based on electroencephalography recordings: A review

J Cao, Y Zhao, X Shan, H Wei, Y Guo… - Human brain …, 2022 - Wiley Online Library
Functional connectivity and effective connectivity of the human brain, representing statistical
dependence and directed information flow between cortical regions, significantly contribute …

Epileptic seizure detection using machine learning: Taxonomy, opportunities, and challenges

MS Farooq, A Zulfiqar, S Riaz - Diagnostics, 2023 - mdpi.com
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 …

A comprehensive comparison of handcrafted features and convolutional autoencoders for epileptic seizures detection in EEG signals

A Shoeibi, N Ghassemi, R Alizadehsani… - Expert Systems with …, 2021 - Elsevier
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 …

Automatic seizure detection using orthogonal matching pursuit, discrete wavelet transform, and entropy based features of EEG signals

A Zarei, BM Asl - Computers in Biology and Medicine, 2021 - Elsevier
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 …

EEG signal processing for epilepsy seizure detection using 5-level Db4 discrete wavelet transform, GA-based feature selection and ANN/SVM classifiers

M Omidvar, A Zahedi, H Bakhshi - Journal of ambient intelligence and …, 2021 - Springer
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 …

An edge-fog computing-enabled lossless EEG data compression with epileptic seizure detection in IoMT networks

AK Idrees, SK Idrees, R Couturier… - IEEE Internet of Things …, 2022 - ieeexplore.ieee.org
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 …

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 …

Epileptic seizure detection with deep EEG features by convolutional neural network and shallow classifiers

W Zeng, L Shan, B Su, S Du - Frontiers in neuroscience, 2023 - frontiersin.org
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 …

Risk of data leakage in estimating the diagnostic performance of a deep-learning-based computer-aided system for psychiatric disorders

HT Lee, HR Cheon, SH Lee, M Shim, HJ Hwang - Scientific Reports, 2023 - nature.com
Deep-learning approaches with data augmentation have been widely used when
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

A Miltiadous, KD Tzimourta, N Giannakeas… - IEEE …, 2022 - ieeexplore.ieee.org
Epilepsy is the only neurological condition for which electroencephalography (EEG) is the
primary diagnostic and important prognostic clinical tool. However, the manual inspection of …