An 8-channel scalable EEG acquisition SoC with patient-specific seizure classification and recording processor

An 8-channel scalable EEG acquisition SoC is presented to continuously detect and record patient-specific seizure onset activities from scalp EEG. The SoC integrates 8 high-dynamic range Analog Front-End (AFE) channels, a machine-learning seizure classification processor and a 64 KB SRAM. The classification processor exploits the Distributed Quad-LUT filter architecture to minimize the area while also minimizing the overhead in power× delay. The AFE employs a Chopper-Stabilized Capacitive Coupled Instrumentation Amplifier to show NEF of 5.1 and noise RTI of 0.91 μ V rms for 0.5-100 Hz bandwidth. The classification processor adopts a support-vector machine as a classifier, with a GBW controller that gives real-time gain and bandwidth feedback to AFE to maintain accuracy. The SoC is verified with the Children's Hospital Boston-MIT EEG database as well as with rapid eye blink pattern detection test. The SoC is implemented in 0.18 7mu; m 1P6M CMOS process occupying 25 mm2, and it shows an accuracy of 84.4% in eye blink classification test, at 2.03 μJ/classification energy efficiency. The 64 KB on chip memory can store up to 120 seconds of raw EEG data. © 1966-2012 IEEE.