A Micro-Power EEG Acquisition SoC With Integrated Feature Extraction Processor for a Chronic Seizure Detection System
Year: 2010
Type: article
Abstract: 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 14× 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.
Institutions: Princeton University, Massachusetts Institute of Technology
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2169766445