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.
Findings support the feasibility of seizure monitoring with reduced montages approximating chronic subscalp device geometries, despite the need for improved detection algorithms, and suggest that EMU-based full-montage performance could help identify candidates for these devices.
Canine epilepsy is shown to impact an estimated 0.6-5.7% of domestic dog populations worldwide, with approximately 25-33% of affected animals undergoing drug resistance despite consistent epileptic medication (1). Current wearable devices are limited to post-onset seizure detection and cannot predict seizures before th...
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 proof-of-concept study applying contrastive self-supervised learning (SSL) to EEG epileptic seizure detection, comparing a SimCLR-style InfoNCE encoder pre-trained on unlabelled EEG against a supervised-only baseline sharing the same backbone, and evaluating both through a framework encompassing discrimination, opera...
It is suggested that ictal scalp EEG combined with machine learning enables accurate and interpretable seizure onset zone classification in patients with focal cortical dysplasia and may serve as a noninvasive decision-support tool to improve presurgical evaluation and guide invasive EEG planning in focal cortical dysp...
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