AI Networking Cookbook: Practical recipes for AI-assisted network automation and development
TL;DR
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.
Seizure prediction is critically important, as it can help prevent serious injuries, improve quality of life, and potentially reduce the risk of SUDEP. Autonomic fluctuations are known to occur during the preictal and early ictal phases; however, the pattern and magnitude of these changes vary considerably across patie...
OBJECTIVES Ambulatory electroencephalography (aEEG) allows prolonged outpatient monitoring and improves seizure detection compared with routine EEG, particularly for subclinical seizures. aEEG is especially valuable in underserved populations, where socioeconomic barriers and comorbidities contribute to delayed diagnos...
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.
OBJECTIVE Subscalp electroencephalographic (EEG) systems with few channels have emerged as promising solutions for ultra-long-term seizure monitoring, but the impact of montage configuration on automated seizure detection is unclear. We compared automated detection performance between full-scalp and simulated reduced m...
Epilepsy is a long-lasting neurological condition that is characterized by repeated seizures and affects millions of people across the world. Traditional methods of diagnosis that are based on manual interpretation of the clinical examinations and electroencephalogram (EEG) are frequently time-consuming and subjective....
BACKGROUND Ambulatory EEG (aEEG) can be high yield, accessible, and cost-effective for prolonged outpatient monitoring, but it is unclear how patient and clinical characteristics can be used to select patients for whom aEEG may be diagnostic. To this end, this study aims to identify factors associated with diagnostic y...
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