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 …

Modified binary salp swarm algorithm in EEG signal classification for epilepsy seizure detection

SM Ghazali, M Alizadeh, J Mazloum… - … Signal Processing and …, 2022 - Elsevier
Epilepsy is a brain disorder characterized by sudden seizures, periodic abnormal and
inappropriate behaviour, and an altered state of consciousness. The visual diagnosis of …

Effective early detection of epileptic seizures through EEG signals using classification algorithms based on t-distributed stochastic neighbor embedding and K-means

KM Alalayah, EM Senan, HF Atlam, IA Ahmed… - Diagnostics, 2023 - mdpi.com
Epilepsy is a neurological disorder in the activity of brain cells that leads to seizures. An
electroencephalogram (EEG) can detect seizures as it contains physiological information of …

Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Review

D Sharifrazi, N Javed, JH Joloudari… - arxiv preprint arxiv …, 2024 - arxiv.org
Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is
assessed by analyzing signal data such as electroencephalography (EEG), which can offer …

On the intersection of signal processing and machine learning: A use case-driven analysis approach

S Aburakhia, A Shami, GK Karagiannidis - arxiv preprint arxiv:2403.17181, 2024 - arxiv.org
Recent advancements in sensing, measurement, and computing technologies have
significantly expanded the potential for signal-based applications, leveraging the synergy …

Novel seizure detection algorithm based on multi-dimension feature selection

F Dong, Z Yuan, D Wu, L Jiang, J Liu, W Hu - Biomedical Signal Processing …, 2023 - Elsevier
In machine learning based seizure detection research studies, the number of features
directly affects the performance of models. In order to decrease the amount of features under …

Learning robust global-local representation from EEG for neural epilepsy detection

X Zhou, C Liu, R Yang, L Zhang, L Zhai… - IEEE Transactions on …, 2024 - ieeexplore.ieee.org
Epilepsy is a life-threatening and challenging neurological disorder, and applying an
electroencephalogram (EEG) is a commonly used clinical approach for its detection …

Spatial-temporal-circulated GLCM and physiological features for in-vehicle people sensing based on IR-UWB radar

X Yang, Y Ding, X Zhang… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In-vehicle people sensing has received considerable attention for preventing overloading
and forgotten children. Impulse radio ultrawideband (IR-UWB) radar is widely applied in …

A novel SVMA and K-NN classifier based optical ML technique for seizure detection

N Deepa, R Naresh, S Anitha, R Suguna… - Optical and Quantum …, 2023 - Springer
Among the most common paroxysmal neurological conditions is epilepsy. When
spontaneous combustion occurs seizure is a defining feature. An epileptic seizure is caused …

Automated detection of Zika and dengue in Aedes aegypti using neural spiking analysis: A machine learning approach

D Sharifrazi, N Javed, R Alizadehsani… - … Signal Processing and …, 2024 - Elsevier
Mosquito-borne diseases present considerable risks to the health of both animals and
humans. Aedes aegypti mosquitoes are the primary vectors for numerous medically …