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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 |
| Appears in Collections: | Staff Publications Elements |
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| VLSI2021_C13_3.pdf | Published version | 764.14 kB | Adobe PDF | OPEN | Published | View/Download |
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