Title
Prediction of neonatal amplitude-integrated EEG based on LSTM method
Abstract
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.
Year
DOI
Venue
2016
10.1109/BIBM.2016.7822568
2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
Keywords
Field
DocType
aEEG,LSTM,prediction,Root Mean Square Error
Data modeling,Pattern recognition,Computer science,Abnormality,Recurrent neural network,Preprocessor,Artificial intelligence,Electroencephalography,Machine learning
Conference
ISSN
ISBN
Citations 
2156-1125
978-1-5090-1612-9
0
PageRank 
References 
Authors
0.34
8
3
Name
Order
Citations
PageRank
Lizhe Liu100.34
Weiting Chen241.19
Guitao Cao35515.03