An intelligent embedded system for paediatric epilepsy detection and management

Epileptic disorder is a well-known nervous system complication in kids, which may even necessitate ongoing observation in addition to immediate treatment. More conventional methods of diagnosis are restricted by ad hoc data collection and the inability to predict. In this paper, an algorithmic framework and electronic technologies are suggested to be integrated into the method of pediatric epilepsy detection, classification, and control. The system uses real-time EEG acquisition, real-time digital signal processing, and machine learning in detecting seizures using embedded systems. Furthermore, an Internet of Things (IoT) supported layer of communication to allow remote observation and alert. Its accuracy in predicting early seizure onset was high when tested on publicly-available EEG datasets and simulated on embedded hardware. The study proves the feasibility of cross-sectoral co-operation as the key to enhancing paediatric healthcare.