A One-Shot Learning, Online-Tuning, Closed-Loop Epilepsy Management SoC with 0.97μJ/Classification and 97.8% Vector-Based Sensitivity
Miaolin Zhang, Lian Zhang, Jeong Hoan Park, Chne-Wuen Tsai, Kian Ann Ng, Longyang Lin, Yilong Dong, Jiamin Li, Tao Tang, Han Wu, Liuhao Wu, Jerald Yoo
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 0.13mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /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.
Topics & Concepts
Closed loopSensitivity (control systems)Latency (audio)Support vector machineArtificial intelligenceComputer scienceEpilepsyEngineeringPsychologyElectronic engineeringNeuroscienceControl engineeringTelecommunicationsEEG and Brain-Computer InterfacesAdvanced Memory and Neural ComputingFerroelectric and Negative Capacitance Devices