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An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yoo, Jerald | - |
dc.contributor.author | Yan, Long | - |
dc.contributor.author | El-Damak, Dina | - |
dc.contributor.author | Bin Altaf, Muhammad Awais | - |
dc.contributor.author | Shoeb, Ali H. | - |
dc.contributor.author | Chandrakasan, Anantha P. | - |
dc.date.accessioned | 2024-05-03T04:34:39Z | - |
dc.date.available | 2024-05-03T04:34:39Z | - |
dc.date.created | 2024-05-02 | - |
dc.date.issued | 2013-01 | - |
dc.identifier.citation | IEEE Journal of Solid-State Circuits, Vol.48 No.1, pp.214-228 | - |
dc.identifier.issn | 0018-9200 | - |
dc.identifier.uri | https://hdl.handle.net/10371/200841 | - |
dc.description.abstract | An 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.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/JSSC.2012.2221220 | - |
dc.citation.journaltitle | IEEE Journal of Solid-State Circuits | - |
dc.identifier.wosid | 000313362400019 | - |
dc.identifier.scopusid | 2-s2.0-84872102825 | - |
dc.citation.endpage | 228 | - |
dc.citation.number | 1 | - |
dc.citation.startpage | 214 | - |
dc.citation.volume | 48 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Yoo, Jerald | - |
dc.type.docType | Article; Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | INSTRUMENTATION AMPLIFIER | - |
dc.subject.keywordPlus | BRAIN-STIMULATION | - |
dc.subject.keywordPlus | SENSOR | - |
dc.subject.keywordAuthor | Continuous health monitoring | - |
dc.subject.keywordAuthor | distributed quad-LUT (DQ-LUT) | - |
dc.subject.keywordAuthor | electroencephalogram (EEG) | - |
dc.subject.keywordAuthor | epilepsy | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | seizure | - |
dc.subject.keywordAuthor | support vector machine (SVM) | - |
dc.subject.keywordAuthor | System-on-Chip (SoC) | - |
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- College of Engineering
- Department of Electrical and Computer Engineering
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