Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/193489
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dc.titleA One-Shot Learning, Online-Tuning, Closed-Loop Epilepsy Management SoC with 0.97uJ/Classification and 97.8% Vector-Based Sensitivity
dc.contributor.authorZhang, Miaolin
dc.contributor.authorZHANG LIAN
dc.contributor.authorPark, Jeonghoan
dc.contributor.authorTSAI CHNE-WUEN
dc.contributor.authorNG KIAN ANN
dc.contributor.authorLin, Longyan
dc.contributor.authorDONG YILONG
dc.contributor.authorLI, Jiamin
dc.contributor.authorTANG TAO
dc.contributor.authorWU HAN
dc.contributor.authorWU, Liuhao
dc.contributor.authorJERALD YOO
dc.date.accessioned2021-07-05T00:12:52Z
dc.date.available2021-07-05T00:12:52Z
dc.date.issued2021-06-13
dc.identifier.citationZhang, Miaolin, ZHANG LIAN, Park, Jeonghoan, TSAI CHNE-WUEN, NG KIAN ANN, Lin, Longyan, DONG YILONG, LI, Jiamin, TANG TAO, WU HAN, WU, Liuhao, JERALD YOO (2021-06-13). A One-Shot Learning, Online-Tuning, Closed-Loop Epilepsy Management SoC with 0.97uJ/Classification and 97.8% Vector-Based Sensitivity. IEEE Symposium on VLSI Circuits. ScholarBank@NUS Repository.
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/193489
dc.description.abstractWe propose a patient-specific closed-loop epilepsy tracking and real-time suppression SoC with the first-in-literature one-shot learning and online tuning. The entire SoC consumes the lowest energy reported to date of 0.97μJ/class. and occupies the smallest area of 0.13mm2/Ch. Verified with CHB-MIT database and a local hospital patient, the 9.8b ENOB 2-Cycle AFE combined with the GTCA-SVM DBE achieves vector-based sensitivity, specificity, and latency of 97.8%, 99.5%, and <1s.
dc.publisherIEEE
dc.sourceElements
dc.typeConference Paper
dc.date.updated2021-07-02T08:12:59Z
dc.contributor.departmentDEPT OF ELECTRICAL & COMPUTER ENGG
dc.contributor.departmentLIFE SCIENCES INSTITUTE
dc.description.sourcetitleIEEE Symposium on VLSI Circuits
dc.description.placeUnited States
dc.published.statePublished
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