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Design and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System

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dc.contributor.authorZhang, Chen-
dc.contributor.authorBin Altaf, Muhammad Awais-
dc.contributor.authorYoo, Jerald-
dc.date.accessioned2024-05-03T04:33:45Z-
dc.date.available2024-05-03T04:33:45Z-
dc.date.created2024-05-02-
dc.date.issued2016-07-
dc.identifier.citationIEEE Journal of Biomedical and Health Informatics, Vol.20 No.4, pp.996-1007-
dc.identifier.issn2168-2194-
dc.identifier.urihttps://hdl.handle.net/10371/200827-
dc.description.abstractThis paper presents the design of an area-and energy-efficient closed-loop machine learning-based patient-specific seizure onset and termination detection algorithm, and its on-chip hardware implementation. Application-and scenario-based tradeoffs are compared and reviewed for seizure detection and suppression algorithm and system which comprises electroencephalography (EEG) data acquisition, feature extraction, classification, and stimulation. Support vector machine achieves a good tradeoff among power, area, patient specificity, latency, and classification accuracy for long-term monitoring of patients with limited training seizure patterns. Design challenges of EEG data acquisition on a multichannel wearable environment for a patch-type sensor are also discussed in detail. Dual-detector architecture incorporates two area-efficient linear support vector machine classifiers along with a weight-and-average algorithm to target high sensitivity and good specificity at once. On-chip implementation issues for a patient-specific transcranial electrical stimulation are also discussed. The system design is verified using CHB-MIT EEG database [1] with a comprehensive measurement criteria which achieves high sensitivity and specificity of 95.1% and 96.2%, respectively, with a small latency of 1 s. It also achieves seizure onset and termination detection delay of 2.98 and 3.82 s, respectively, with seizure length estimation error of 4.07 s.-
dc.language영어-
dc.publisherInstitute of Electrical and Electronics Engineers Inc.-
dc.titleDesign and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System-
dc.typeArticle-
dc.identifier.doi10.1109/JBHI.2016.2553368-
dc.citation.journaltitleIEEE Journal of Biomedical and Health Informatics-
dc.identifier.wosid000380128300004-
dc.identifier.scopusid2-s2.0-84978252254-
dc.citation.endpage1007-
dc.citation.number4-
dc.citation.startpage996-
dc.citation.volume20-
dc.description.isOpenAccessN-
dc.contributor.affiliatedAuthorYoo, Jerald-
dc.type.docTypeArticle-
dc.description.journalClass1-
dc.subject.keywordPlusDIRECT-CURRENT STIMULATION-
dc.subject.keywordPlusVAGUS NERVE-STIMULATION-
dc.subject.keywordPlusEEG ACQUISITION SOC-
dc.subject.keywordPlusBRAIN-STIMULATION-
dc.subject.keywordPlusEPILEPSY-
dc.subject.keywordPlusCLASSIFICATION-
dc.subject.keywordPlusSUPPRESSION-
dc.subject.keywordPlusPROCESSOR-
dc.subject.keywordPlusSIGNALS-
dc.subject.keywordPlusCMOS-
dc.subject.keywordAuthorDual-detector architecture (D(2)A)-
dc.subject.keywordAuthorepileptic seizure-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorpatient-specific-
dc.subject.keywordAuthorscalp electroencephalography (EEG)-
dc.subject.keywordAuthorseizure onset and termination detection-
dc.subject.keywordAuthortranscranial electrical stimulation (tES)-
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  • College of Engineering
  • Department of Electrical and Computer Engineering
Research Area Biomedical Applications, Energy-Efficient Integrated Circuits

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