Neonatal Seizures Detection using Stationary Wavelet Transform and Deep Neural Networks: Preliminary Results

The increasing use of Electroencephalography (EEG) in the field of pediatric neurology allows more accurate and precise diagnosis of several cerebral pathologies, mainly in Neonatal Intensive Care Units (NICUs), where it represents the gold-standard for the diagnosis of neonatal epileptic seizures. However, EEG interpretation is time consuming and requires highly specialized staff. For this reason, in the last years there was a growing interest in the development of systems for automatic and fast detection of neonatal epileptic seizures. To this aim, we propose here hybrid systems that combines techniques related to the Stationary Wavelet Transform (SWT) as a support to deep-learning algorithms such as Convolutional Neural Networks and Fully Convolutional Networks. The proposed methods are validated on a public dataset of NICUs seizures recorded at the Helsinki University Hospital. Results are encouraging both in terms of Area Under the receiver-operating Curve, AUC (81%), Good Detection Rate, GDR (77%) and False Detection per hour, FD/h (1.6). Actually, the SWT step increases the performance of the proposed methods of about 5% for the AUC as compared to considering the raw EEG time-series only. These results, though preliminary, represent a significant step forward in solving the problem of neonatal seizure detection.