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.

[1]  Jürgen Schmidhuber,et al.  Learning Precise Timing with LSTM Recurrent Networks , 2003, J. Mach. Learn. Res..

[2]  Navdeep Jaitly,et al.  Hybrid speech recognition with Deep Bidirectional LSTM , 2013, 2013 IEEE Workshop on Automatic Speech Recognition and Understanding.

[3]  Yusuf Uzzaman Khan,et al.  Seizure prediction using statistical dispersion measures of intracranial EEG , 2014, Biomed. Signal Process. Control..

[4]  Tao Yang,et al.  Automated classification of neonatal amplitude-integrated EEG based on gradient boosting method , 2016, Biomed. Signal Process. Control..

[5]  Ingmar Rosén,et al.  Continuous brain-function monitoring: state of the art in clinical practice. , 2006, Seminars in fetal & neonatal medicine.

[6]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[7]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[8]  Jürgen Schmidhuber,et al.  Learning to forget: continual prediction with LSTM , 1999 .

[9]  Zoubin Ghahramani,et al.  A Theoretically Grounded Application of Dropout in Recurrent Neural Networks , 2015, NIPS.

[10]  J C Shaw,et al.  The EEG as a Measure of Cerebral Functional Organization , 1977, British Journal of Psychiatry.

[11]  Kai Huang,et al.  Classification of neonatal amplitude-integrated EEG using random forest model with combined feature , 2013, 2013 IEEE International Conference on Bioinformatics and Biomedicine.

[12]  F. Gers,et al.  Long short-term memory in recurrent neural networks , 2001 .

[13]  Wojciech Zaremba,et al.  An Empirical Exploration of Recurrent Network Architectures , 2015, ICML.

[14]  Hermann Ney,et al.  From Feedforward to Recurrent LSTM Neural Networks for Language Modeling , 2015, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[15]  William P. Marnane,et al.  Discriminative and Generative Classification Techniques Applied to Automated Neonatal Seizure Detection , 2013, IEEE Journal of Biomedical and Health Informatics.

[16]  Yoshua Bengio,et al.  Gradient Flow in Recurrent Nets: the Difficulty of Learning Long-Term Dependencies , 2001 .