Neonatal Seizure Detection from EEG Signals using Deep Learning Methods

Neonatal epilepsy, a serious neurological condition in newborns, is typically marked by irregular brain activity that results in repeated seizures. This could have detrimental effect on neurodevelopment and may raise risk of cognitive and behavioral abnormalities. In order to prevent long-term neurological damage and developmental delays, an accurate detection of such seizures is crucial. Neonatal seizures can be subtle and may not exhibit any clear physical signs and hence, complicating timely diagnosis and treatment. To assist in diagnosis, electroencephalogram (EEG) can analyze, but manual inspection is time-consuming and burdensome for neurologists. Therefore, an automated EEG review would enable more frequent monitoring of neonates at risk. Hence, an automated system for detecting seizures has been created, trained on multichannel annotated EEG recordings from 79 full-term neonates who were admitted to Helsinki University Hospital. The system extracts wavelet coefficients from 10 seconds EEG segments using multilevel Daubechies 4 (db4) wavelet decompositionand uses these features to train a 2-Dimensional Convolutional Neural Network (2D-CNN). This model has achieved 100% accuracy, sensitivity, and F1-score, outperformed existing state-of-the-art methods and provided a reliable solution for automated seizure detection.