TL;DR
A clinically interpretable aEEG-CSA algorithm is feasible for neonatal seizure detection by extracting standard EEG features and coupling these features with a supervised ML classifier.
A neonatal seizure detection algorithm that uses a combination of aEEG and CSA clinical features can capture seizures in HIE patients and performance across seizure patients is not correlated with seizure duration.
A Deep Hybrid Neural Network framework that combines Convolutional Neural Networks (CNN) with the Aquila Optimizer (AO) for the automatic detection of epileptic seizures utilizing EEG data in MATLAB is introduced.
Epilepsy and migraine are prone to clinical misdiagnosis due to overlapping clinical manifestations, while the limited availability of electroencephalogram (EEG) data further complicates accurate differential diagnosis. To address this challenge, this study proposes a three-class classification framework for epilepsy,...
This study systematically introduces and evaluates 25 less-explored time-domain features, 13 of which have no documented precedent as classification features in scalp EEG seizure detection, against 25 classical features and their 50-feature combination.
A compact composite feature termed the Seizure Intensity Index (SII) together with an extended representation incorporating additional theta and alpha band descriptors is proposed together with an extended representation incorporating additional theta and alpha band descriptors for cross-patient seizure detection.
Due to EEG overlap, brain tumour-induced seizures (BTIS) and primary epileptic seizures (ES) are difficult to distinguish. This paper introduces an AI-enhanced BCI model that uses hybrid deep learning architectures to differentiate accurately. We used CNNs for spatial feature extraction and Transformer-based attention...
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