Anisotropic Diffusion-based Analog CNN Architecture for Continuous EEG Monitoring

This study proposes an ultra-low-power processing approach for early seizure detection using electroencephalogram (EEG) signal processing. Traditional methods of EEG processing are power-hungry and resource-intensive, making them impractical for long-term, real-time monitoring. We propose a two-tier wireless sensor network architecture with all-analog in-situ processing at the sensor level and Cluster-Heads (CHs) to compile the data into meaningful information. The paper introduces a novel all-analog Convolutional Processing Unit (CvPU) that utilizes anisotropic diffusion properties in electrical circuits and a neural-network architecture for EEG-based seizure detection. The power consumption of the proposed architecture is estimated to be 1 to 3 orders of magnitude lower than contemporary digital and hybrid analog-digital approaches. The proposed approach is cost-effective, generates a comprehensive picture of an individual’s brain health, and can be applied in other physiological monitoring areas, including athletic fitness assessments and personal-health monitoring.