Low-Power Real-Time Seizure Monitoring Using AI-Assisted Sonification of Neonatal EEG

Abstract

Detecting seizures in neonates requires continuous electroencephalography (EEG) monitoring, a costly process that demands trained experts. Although recent advancements in machine learning offer promising solutions for automated seizure detection, the opaque nature of these algorithms poses significant challenges to their adoption in healthcare settings. A prior study demonstrated that integrating machine learning with sonification—an interpretation method that converts bio-signals into sound—can mitigate the black-box problem while enhancing seizure detection performance. This AI-assisted sonification algorithm can provide a valuable complementary tool in seizure monitoring besides the traditional visualization method. A low-power and affordable implementation of the algorithm is presented in this study using a microcontroller. To improve its practicality, we also introduce a real-time design that allows the sonification algorithm to function in parallel with data acquisition. The system consumes 12 mW in average, making it suitable for a battery-powered device.