Engineering nonlinear epileptic biomarkers using deep learning and Benford’s law
Engineering nonlinear epileptic biomarkers using deep learning and Benford’s law
About this item
Full title
Author / Creator
Publisher
London: Nature Publishing Group UK
Journal title
Language
English
Formats
Publication information
Publisher
London: Nature Publishing Group UK
Subjects
More information
Scope and Contents
Contents
In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their frequency responses to 16 widely used engineered metrics to interpret their properties: epileptogenicity index (EI), phase locked high gamma (PLHG), time and frequency domain Cho Gaines distance (TDCG...
Alternative Titles
Full title
Engineering nonlinear epileptic biomarkers using deep learning and Benford’s law
Authors, Artists and Contributors
Identifiers
Primary Identifiers
Record Identifier
TN_cdi_doaj_primary_oai_doaj_org_article_0e49a04536e447f8832164d2fd664305
Permalink
https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_doaj_primary_oai_doaj_org_article_0e49a04536e447f8832164d2fd664305
Other Identifiers
ISSN
2045-2322
E-ISSN
2045-2322
DOI
10.1038/s41598-022-09429-w
