EEG in neonatal seizures: where to look and what to see

Combining EEG Features and Convolutional Autoencoder for Neonatal Seizure Detection

Neonatal epilepsy is a common emergency phenomenon in neonatal intensive care units (NICUs), which requires timely attention, early identification, and treatment. Traditional detection methods mostly use supervised learning with enormous labeled data. Hence, this study offers a semi-supervised hybrid architecture for detecting seizures, which combines the extracted electroencephalogram (EEG) feature dataset and convolutional autoencoder, called Fd-CAE. First, various features in the time domain and entropy domain are extracted to characterize the EEG signal, which helps distinguish epileptic seizures subsequently. Then, the unlabeled EEG features are fed into the convolutional autoencoder (CAE) for training, which effectively represents EEG features by optimizing the loss between the input and output features. This unsupervised feature learning process can better combine and optimize EEG features from unlabeled data. After that, the pre-trained encoder part of the model is used for further feature learning of labeled data to obtain its low-dimensional feature representation and achieve classification. This model is performed on the neonatal EEG dataset collected at the University of Helsinki Hospital, which has a high discriminative ability to detect seizures, with an accuracy of 92.34%, precision of 93.61%, recall rate of 98.74%, and F1-score of 95.77%, respectively. The results show that unsupervised learning by CAE is beneficial to the characterization of EEG signals, and the proposed Fd-CAE method significantly improves classification performance.

Questions and Controversies in Neonatal Seizures

Neonatal seizures are relatively common, but their diagnosis and management remain challenging. We reviewed the scientific literature on neonatal seizures from July 1973 to November 2023. Several parameters were considered, including pathophysiology, diagnostic criteria, electroencephalographic findings and treatment. Recent classification system of seizures and epilepsies in the newborn, as well as treatment recommendations of neonatal seizures, have been proposed. Nonetheless, the approach to neonatal seizures varies among clinicians and centres, including detection, investigation, treatment and follow-up of patients. There are still many issues on the diagnosis and treatment of neonatal seizures, including the meaning or relevance of some electroencephalographic findings, the precise estimation of the seizure burden, the limited efficacy and side effects risk of antiseizure medications, and the best measures to establish the outcome.