A novel neonatal seizure detection system is proposed including work in the areas of Independent Component Analysis, feature extraction, probability and classification networks. The system comprises of a preprocessing stage to reduce the effect of EEG artifacts and incorporate multichannel analysis and data reduction. A feature extraction stage examines the EEG using techniques from various signal processing approaches. A Probability Estimator compares current and past features to emphasise changes in the state of the EEG. Finally, a classification stage uses the results from the probability estimator to make a decision as to whether the EEG is non-seizure or seizure. Results show promising performance, detecting 45 of 46 seizures in the test data with low false detection rates.