Publications

Detailed Information

An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor

DC Field Value Language
dc.contributor.authorYoo, Jerald-
dc.contributor.authorYan, Long-
dc.contributor.authorEl-Damak, Dina-
dc.contributor.authorBin Altaf, Muhammad Awais-
dc.contributor.authorShoeb, Ali H.-
dc.contributor.authorChandrakasan, Anantha P.-
dc.date.accessioned2024-05-03T04:34:39Z-
dc.date.available2024-05-03T04:34:39Z-
dc.date.created2024-05-02-
dc.date.issued2013-01-
dc.identifier.citationIEEE Journal of Solid-State Circuits, Vol.48 No.1, pp.214-228-
dc.identifier.issn0018-9200-
dc.identifier.urihttps://hdl.handle.net/10371/200841-
dc.description.abstractAn 8-channel scalable EEG acquisition SoC is presented to continuously detect and record patient-specific seizure onset activities from scalp EEG. The SoC integrates 8 high-dynamic range Analog Front-End (AFE) channels, a machine-learning seizure classification processor and a 64 KB SRAM. The classification processor exploits the Distributed Quad-LUT filter architecture to minimize the area while also minimizing the overhead in power x delay. The AFE employs a Chopper-Stabilized Capacitive Coupled Instrumentation Amplifier to show NEF of 5.1 and noise RTI of 0.91 mu V-rms for 0.5-100 Hz bandwidth. The classification processor adopts a support-vector machine as a classifier, with a GBW controller that gives real-time gain and bandwidth feedback to AFE to maintain accuracy. The SoC is verified with the Children's Hospital Boston-MIT EEG database as well as with rapid eye blink pattern detection test. The SoC is implemented in 0.18 mu m 1P6M CMOS process occupying 25 mm(2), and it shows an accuracy of 84.4% in eye blink classification test, at 2.03 mu J/classification energy efficiency. The 64 KB on chip memory can store up to 120 seconds of raw EEG data.-
dc.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.titleAn 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor-
dc.typeArticle-
dc.identifier.doi10.1109/JSSC.2012.2221220-
dc.citation.journaltitleIEEE Journal of Solid-State Circuits-
dc.identifier.wosid000313362400019-
dc.identifier.scopusid2-s2.0-84872102825-
dc.citation.endpage228-
dc.citation.number1-
dc.citation.startpage214-
dc.citation.volume48-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorYoo, Jerald-
dc.type.docTypeArticle; Proceedings Paper-
dc.description.journalClass1-
dc.subject.keywordPlusINSTRUMENTATION AMPLIFIER-
dc.subject.keywordPlusBRAIN-STIMULATION-
dc.subject.keywordPlusSENSOR-
dc.subject.keywordAuthorContinuous health monitoring-
dc.subject.keywordAuthordistributed quad-LUT (DQ-LUT)-
dc.subject.keywordAuthorelectroencephalogram (EEG)-
dc.subject.keywordAuthorepilepsy-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorseizure-
dc.subject.keywordAuthorsupport vector machine (SVM)-
dc.subject.keywordAuthorSystem-on-Chip (SoC)-
Appears in Collections:
Files in This Item:
There are no files associated with this item.

Related Researcher

Yoo, Jerald Image

Yoo, Jerald유담
부교수
  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits

Altmetrics

Item View & Download Count

  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Share