A neural network based infant monitoring system to facilitate diagnosis of epileptic seizures

In this paper, we propose an infant monitoring system that automatically detects epileptic seizures in domestic and hospital environments. The proposed system measures the movements and electroencephalogram (EEG) signals of an infant using a video camera and an electroencephalograph. Seizure features are then extracted from the video images and EEG signals, and the evaluation indices based on medical knowledge are calculated from the features. The system employs a probabilistic neural network for the automatic detection of seizures, thereby allowing the choice/combination of evaluation indices appropriate for the environment via network training. We tested the system in simulated domestic and hospital environments. The validity of the proposed system was reinforced by the results of comparisons with clinical diagnoses.