Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/193489
Title: A One-Shot Learning, Online-Tuning, Closed-Loop Epilepsy Management SoC with 0.97uJ/Classification and 97.8% Vector-Based Sensitivity
Authors: Zhang, 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 
Issue Date: 13-Jun-2021
Publisher: IEEE
Citation: Zhang, 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.
Abstract: We 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.
Source Title: IEEE Symposium on VLSI Circuits
URI: https://scholarbank.nus.edu.sg/handle/10635/193489
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