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Design and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Zhang, Chen | - |
dc.contributor.author | Bin Altaf, Muhammad Awais | - |
dc.contributor.author | Yoo, Jerald | - |
dc.date.accessioned | 2024-05-03T04:33:45Z | - |
dc.date.available | 2024-05-03T04:33:45Z | - |
dc.date.created | 2024-05-02 | - |
dc.date.issued | 2016-07 | - |
dc.identifier.citation | IEEE Journal of Biomedical and Health Informatics, Vol.20 No.4, pp.996-1007 | - |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://hdl.handle.net/10371/200827 | - |
dc.description.abstract | This 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.language | 영어 | - |
dc.publisher | Institute of Electrical and Electronics Engineers Inc. | - |
dc.title | Design and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/JBHI.2016.2553368 | - |
dc.citation.journaltitle | IEEE Journal of Biomedical and Health Informatics | - |
dc.identifier.wosid | 000380128300004 | - |
dc.identifier.scopusid | 2-s2.0-84978252254 | - |
dc.citation.endpage | 1007 | - |
dc.citation.number | 4 | - |
dc.citation.startpage | 996 | - |
dc.citation.volume | 20 | - |
dc.description.isOpenAccess | N | - |
dc.contributor.affiliatedAuthor | Yoo, Jerald | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | DIRECT-CURRENT STIMULATION | - |
dc.subject.keywordPlus | VAGUS NERVE-STIMULATION | - |
dc.subject.keywordPlus | EEG ACQUISITION SOC | - |
dc.subject.keywordPlus | BRAIN-STIMULATION | - |
dc.subject.keywordPlus | EPILEPSY | - |
dc.subject.keywordPlus | CLASSIFICATION | - |
dc.subject.keywordPlus | SUPPRESSION | - |
dc.subject.keywordPlus | PROCESSOR | - |
dc.subject.keywordPlus | SIGNALS | - |
dc.subject.keywordPlus | CMOS | - |
dc.subject.keywordAuthor | Dual-detector architecture (D(2)A) | - |
dc.subject.keywordAuthor | epileptic seizure | - |
dc.subject.keywordAuthor | machine learning | - |
dc.subject.keywordAuthor | patient-specific | - |
dc.subject.keywordAuthor | scalp electroencephalography (EEG) | - |
dc.subject.keywordAuthor | seizure onset and termination detection | - |
dc.subject.keywordAuthor | transcranial electrical stimulation (tES) | - |
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- College of Engineering
- Department of Electrical and Computer Engineering
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