Prediction of neonatal amplitude-integrated EEG based on LSTM method

Amplitude-integrated EEG (aEEG) is becoming more and more useful in the monitoring of clinically ill neonates. If there is a method that can predict neonatal aEEG signals, doctors can forecast the possible abnormality of neonates' brain functions in advance and give early intervention. However, no such research on the prediction of aEEG signals has been found in the literature. In this paper, we combine aEEG signals with Long-Short Time Memory (LSTM) model and propose a method to predict aEEG signals based on LSTM. All of the aEEG signals after preprocessing were used as the input of the LSTM, a type of recurrent neural networks which can process long term signals with high accuracy. To assess the method, several experiments were conducted on 276 neonatal aEEG tracings including 217 normal cases and 59 abnormal ones. Experimental results show that the predicted aEEG signals are very close to the real aEEG signals. Our LSTM-based method might therefore help predict neonatal brain disorders in NICUs.