A Portable EEG Monitoring System for Neonatal Seizures

This paper introduces a portable electroencephalogram (EEG) monitoring system for neonatal seizures. The hardware circuit can acquire 8 channels of EEG data or expand to 32 channels. It is characterized in high accuracy, small size, and low power consumption, which can meet the needs of long-term bedside monitoring of newborns. Users can choose different modes to view the historical data of the acquired EEG signals, or real-time data transmitted via WiFi. To further analyze and process the data, this paper introduces a machine learning-based scheme to automatically distinguish between normal and abnormal amplitude-integrated EEG (aEEG) signals. The program extracts the effective feature representation from the data and trains the model. The model can predict the class of unknown aEEG samples, which is helpful for further clinical detection of neonatal brain conditions.