Show simple item record

dc.creatorGolmohammadi, M
dc.creatorHarati Nejad Torbati, AH
dc.creatorLopez de Diego, S
dc.creatorObeid, I
dc.creatorPicone, J
dc.date.accessioned2020-12-16T18:10:56Z
dc.date.available2020-12-16T18:10:56Z
dc.date.issued2019-02-01
dc.identifier.issn1662-5161
dc.identifier.issn1662-5161
dc.identifier.doihttp://dx.doi.org/10.34944/dspace/4558
dc.identifier.otherHO5AU (isidoc)
dc.identifier.other30914936 (pubmed)
dc.identifier.urihttp://hdl.handle.net/20.500.12613/4576
dc.description.abstract© 2019 Golmohammadi, Harati Nejad Torbati, Lopez de Diego, Obeid and Picone. Brain monitoring combined with automatic analysis of EEGs provides a clinical decision support tool that can reduce time to diagnosis and assist clinicians in real-time monitoring applications (e.g., neurological intensive care units). Clinicians have indicated that a sensitivity of 95% with specificity below 5% was the minimum requirement for clinical acceptance. In this study, a high-performance automated EEG analysis system based on principles of machine learning and big data is proposed. This hybrid architecture integrates hidden Markov models (HMMs) for sequential decoding of EEG events with deep learning-based post-processing that incorporates temporal and spatial context. These algorithms are trained and evaluated using the Temple University Hospital EEG, which is the largest publicly available corpus of clinical EEG recordings in the world. This system automatically processes EEG records and classifies three patterns of clinical interest in brain activity that might be useful in diagnosing brain disorders: (1) spike and/or sharp waves, (2) generalized periodic epileptiform discharges, (3) periodic lateralized epileptiform discharges. It also classifies three patterns used to model the background EEG activity: (1) eye movement, (2) artifacts, and (3) background. Our approach delivers a sensitivity above 90% while maintaining a specificity below 5%. We also demonstrate that this system delivers a low false alarm rate, which is critical for any spike detection application.
dc.format.extent76-
dc.language.isoeng
dc.relation.haspartFrontiers in Human Neuroscience
dc.relation.isreferencedbyFrontiers Media SA
dc.rightsCC BY
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectelectroencephalography
dc.subjectEEG
dc.subjecthidden markov models
dc.subjectHMM
dc.subjectdeep learning
dc.subjectstochastic denoising autoencoders
dc.subjectSdA
dc.subjectautomatic detection
dc.titleAutomatic analysis of EEGs using big data and hybrid deep learning architectures
dc.typeArticle
dc.type.genreJournal Article
dc.relation.doi10.3389/fnhum.2019.00076
dc.ada.noteFor Americans with Disabilities Act (ADA) accommodation, including help with reading this content, please contact scholarshare@temple.edu
dc.creator.orcidObeid, Iyad|0000-0002-5796-843X
dc.date.updated2020-12-16T18:10:52Z
refterms.dateFOA2020-12-16T18:10:57Z


Files in this item

Thumbnail
Name:
Automatic Analysis of EEGs Using ...
Size:
3.152Mb
Format:
PDF

This item appears in the following Collection(s)

Show simple item record

CC BY
Except where otherwise noted, this item's license is described as CC BY