A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System

This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chron...

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Main Authors: Verma, Naveen, Shoeb, Ali H., Bohorquez, Jose L., Dawson, Joel L., Guttag, John V., Chandrakasan, Anantha P.
Other Authors: Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Format: Article
Language:en_US
Published: Institute of Electrical and Electronics Engineers (IEEE) 2012
Online Access:http://hdl.handle.net/1721.1/74119
https://orcid.org/0000-0003-0992-0906
https://orcid.org/0000-0002-5977-2748
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author Verma, Naveen
Shoeb, Ali H.
Bohorquez, Jose L.
Dawson, Joel L.
Guttag, John V.
Chandrakasan, Anantha P.
author2 Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
author_facet Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Verma, Naveen
Shoeb, Ali H.
Bohorquez, Jose L.
Dawson, Joel L.
Guttag, John V.
Chandrakasan, Anantha P.
author_sort Verma, Naveen
collection MIT
description This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. The SoC integrates an instrumentation amplifier, ADC, and digital processor that streams features-vectors to a central device where seizure detection is performed via a machine-learning classifier. The instrumentation-amplifier uses chopper-stabilization in a topology that achieves high input-impedance and rejects large electrode-offsets while operating at 1 V; the ADC employs power-gating for low energy-per-conversion while using static-biasing for comparator precision; the EEG feature extraction processor employs low-power hardware whose parameters are determined through validation via patient data. The integration of sensing and local processing lowers system power by 14à by reducing the rate of wireless EEG data transmission. Feature vectors are derived at a rate of 0.5 Hz, and the complete one-channel SoC operates from a 1 V supply, consuming 9 ¿ J per feature vector.
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spelling mit-1721.1/741192022-09-30T22:26:11Z A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System Verma, Naveen Shoeb, Ali H. Bohorquez, Jose L. Dawson, Joel L. Guttag, John V. Chandrakasan, Anantha P. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology. Microsystems Technology Laboratories Chandrakasan, Anantha P. Shoeb, Ali H. Bohorquez, Jose L. Dawson, Joel L. Guttag, John V. Chandrakasan, Anantha P. This paper presents a low-power SoC that performs EEG acquisition and feature extraction required for continuous detection of seizure onset in epilepsy patients. The SoC corresponds to one EEG channel, and, depending on the patient, up to 18 channels may be worn to detect seizures as part of a chronic treatment system. The SoC integrates an instrumentation amplifier, ADC, and digital processor that streams features-vectors to a central device where seizure detection is performed via a machine-learning classifier. The instrumentation-amplifier uses chopper-stabilization in a topology that achieves high input-impedance and rejects large electrode-offsets while operating at 1 V; the ADC employs power-gating for low energy-per-conversion while using static-biasing for comparator precision; the EEG feature extraction processor employs low-power hardware whose parameters are determined through validation via patient data. The integration of sensing and local processing lowers system power by 14à by reducing the rate of wireless EEG data transmission. Feature vectors are derived at a rate of 0.5 Hz, and the complete one-channel SoC operates from a 1 V supply, consuming 9 ¿ J per feature vector. 2012-10-18T20:26:37Z 2012-10-18T20:26:37Z 2010-04 2010-03 Article http://purl.org/eprint/type/JournalArticle 0018-9200 1558-173X http://hdl.handle.net/1721.1/74119 Verma, Naveen et al. “A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System.” IEEE Journal of Solid-State Circuits 45.4 (2010): 804–816. © 2010 IEEE https://orcid.org/0000-0003-0992-0906 https://orcid.org/0000-0002-5977-2748 en_US http://dx.doi.org/10.1109/jssc.2010.2042245 IEEE Journal of Solid-State Circuits Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf Institute of Electrical and Electronics Engineers (IEEE) IEEE
spellingShingle Verma, Naveen
Shoeb, Ali H.
Bohorquez, Jose L.
Dawson, Joel L.
Guttag, John V.
Chandrakasan, Anantha P.
A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
title A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
title_full A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
title_fullStr A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
title_full_unstemmed A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
title_short A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
title_sort micro power eeg acquisition soc with integrated feature extraction processor for a chronic seizure detection system
url http://hdl.handle.net/1721.1/74119
https://orcid.org/0000-0003-0992-0906
https://orcid.org/0000-0002-5977-2748
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