ArticleIEEE journal of translational engineering in health and medicine2019
Classification of Scalp EEG States Prior to Clinical Seizure Onset.
Article in IEEE journal of translational engineering in health and medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- PyHFO: lightweight deep learning-powered end-to-end high-frequency oscillations analysis application.Journal of neural engineering · 2024Article
- Biomimetic Deep Learning Networks With Applications to Epileptic Spasms and Seizure Prediction.IEEE transactions on bio-medical engineering · 2024Article
- Pediatric Seizure Prediction in Scalp EEG Using a Multi-Scale Neural Network With Dilated Convolutions.IEEE journal of translational engineering in health and medicine · 2022Article
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectiveTo investigate the feasibility of improving the performance of an EEG-based multistate classifier (MSC) previously proposed by our group.
resultsUsing the random forest (RF) classifiers on the previously reported dataset of patients, but with three improvements to classification logic, the specificity of our alarm algorithm improves from 82.4% to 92.0%, and sensitivity from 87.9% to 95.2%. DISCUSSION: The MSC could be a useful approach for seizure-monitoring both in the clinic and at home.
methodsThree improvements to the MSC are described. Firstly, an additional check using RF outputs is made prior to alarm to confirm increasing probability of a seizure onset state. Secondly, a post-alarm detection horizon that accounts for the seizure state duration is implemented. Thirdly, the alarm decision window is kept constant.
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