SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier-Reference-Cited by-同舟云学术

SOUL: An Energy-Efficient Unsupervised Online Learning Seizure Detection Classifier

Published:2022-08 Issue:8 Volume:57 Page:2532-2544
ISSN:0018-9200
Container-title:IEEE Journal of Solid-State Circuits
language:
Short-container-title:IEEE J. Solid-State Circuits

Author:

Chua Adelson¹^ORCID,Jordan Michael I.²^ORCID,Muller Rikky¹

Affiliation:

1. Department of Electrical Engineering and Computer Sciences, University of California at Berkeley, Berkeley, CA, USA

2. Department of Electrical Engineering and Computer Sciences and the Department of Statistics, University of California at Berkeley, Berkeley, CA, USA

Funder

National Science Foundation (NSF) Faculty Early Career Development Program

Wagner Foundation

Weill Neurohub, in part the Army Research Office

Department of Science and Technology - Science Education Institute

University of the Philippines Diliman

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering

Link

https://ieeexplore.ieee.org/ielam/4/9837319/9773289-aam.pdf

Reference27 articles.

1. Epilepsyecosystem.org: Crowd-sourcing reproducible seizure prediction with long-term human intracranial EEG;kuhlmann;Brain,2018

2. An 8-Channel Scalable EEG Acquisition SoC With Patient-Specific Seizure Classification and Recording Processor

3. Design and Implementation of an On-Chip Patient-Specific Closed-Loop Seizure Onset and Termination Detection System

4. A recursive-memory brain-state classifier with 32-channel track-and-zoom $\Delta^{2} \Sigma$ ADCs and charge-balanced programmable waveform neurostimulators;o’leary;IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers,2018

5. 26.3 A closed-loop neuromodulation chipset with 2-level classification achieving 1.5 Vpp CM interference tolerance, 35dB stimulation artifact rejection in 0.5ms and 97.8% sensitivity seizure detection;wang;IEEE Int Solid-State Circuits Conf (ISSCC) Dig Tech Papers,2020

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4. A review of automatic detection of epilepsy based on EEG signals;Journal of Semiconductors;2023-12-01

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