RSSI Amplifier Design for a Feature Extraction Technique to Detect Seizures with Analog Computing

Advances of machine learning algorithms have led to improvements of seizure detection capabilities in monitoring systems based on electroencephalography (EEG). Seizure detection hardware requires accurate feature extraction, which is conventionally done in the digital domain by extracting power in different EEG frequency bands over a particular time window. This paper presents an analog counterpart to digital feature extraction. A received signal strength indicator (RSSI) circuit is used for extracting EEG power features in the analog domain. A high-precision RSSI circuit was designed in the sub-threshold domain with ultra-low power consumption and low sensitivity to process-voltage-temperature variations with CMOS technology. Simulation results show that the RSSI circuit consumes 24 nW power, and has a dynamic range of 53 dB with a linearity error of ± 0.5 dB, sufficient to accurately extract features for seizure classification. The analysis of 16 hours of patient EEG data indicates a seizure classification accuracy of 94%, and a non-seizure classification of 86%.