A Patient-Specific Closed-Loop Epilepsy Management SoC With One-Shot Learning and Online Tuning
Miaolin Zhang, Lian Zhang, Chne-Wuen Tsai, Jerald Yoo
Abstract
Epilepsy treatment in clinical practices with surface electroencephalogram (EEG) often faces training dataset shortage issue, which is aggravated by seizure pattern variation among patients. To facilitate future optimization of the detection accuracy as new datasets are available, a fully programmable patient-specific closed-loop epilepsy tracking and suppression system-on-chip (SoC) is proposed with the first-in-literature one-shot learning and online tuning to the best of our knowledge. The proposed two-cycle analog front end (2C-AFE) obtains a 9.8-b effective number of bits (ENOB) with 8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> capacitive digital-to-analog converter (CAPDAC) area reduction and 4 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> switching energy saving compared to a conventional 10-b SAR with an identical unit capacitor size. The entire SoC with 16 surface EEG recording channels consumes an ultra-low energy of 0.97 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\mu \text{J}$ </tex-math></inline-formula> /class and occupies a miniaturized area of 0.13 mm <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> /ch. in 40-nm CMOS, achieving real-time concurrent seizure detection and raw EEG recording. Verified with the CHB-MIT database, the guided time–channel averaging (GTCA) neural processor achieves the vector-based sensitivity, the specificity, and the latency of 97.8%, 99.5%, and < 1 s, respectively. The initial one-shot learning and follow-up online tuning function is validated with the EEG recording from a local hospital patient, which demonstrates a 1.8 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\times $ </tex-math></inline-formula> vector-based sensitivity boost.