Prediction of neonatal amplitude-integrated EEG based on LSTM method

Lizhe Liu; Weiting Chen; Guitao Cao

doi:10.1109/BIBM.2016.7822568

Prediction of neonatal amplitude-integrated EEG based on LSTM method

Lizhe Liu
, Weiting Chen
, Guitao Cao

Software Engineering Institute

East China Normal University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

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.

Original language	English
Title of host publication	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Editors	Kevin Burrage, Qian Zhu, Yunlong Liu, Tianhai Tian, Yadong Wang, Xiaohua Tony Hu, Qinghua Jiang, Jiangning Song, Shinichi Morishita, Kevin Burrage, Guohua Wang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	497-500
Number of pages	4
ISBN (Electronic)	9781509016105
DOIs	https://doi.org/10.1109/BIBM.2016.7822568
State	Published - 17 Jan 2017
Event	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China Duration: 15 Dec 2016 → 18 Dec 2016

Publication series

Name	Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Conference

Conference	2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
Country/Territory	China
City	Shenzhen
Period	15/12/16 → 18/12/16

Keywords

AEEG
LSTM
Prediction
Root Mean Square Error

Access to Document

10.1109/BIBM.2016.7822568

Cite this

Liu, L., Chen, W., & Cao, G. (2017). Prediction of neonatal amplitude-integrated EEG based on LSTM method. In K. Burrage, Q. Zhu, Y. Liu, T. Tian, Y. Wang, X. T. Hu, Q. Jiang, J. Song, S. Morishita, K. Burrage, & G. Wang (Eds.), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 497-500). Article 7822568 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822568

Liu, Lizhe ; Chen, Weiting ; Cao, Guitao. / Prediction of neonatal amplitude-integrated EEG based on LSTM method. Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. editor / Kevin Burrage ; Qian Zhu ; Yunlong Liu ; Tianhai Tian ; Yadong Wang ; Xiaohua Tony Hu ; Qinghua Jiang ; Jiangning Song ; Shinichi Morishita ; Kevin Burrage ; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. pp. 497-500 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

@inproceedings{512f69ab3a4a4123863e587f242cac77,

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.",

keywords = "AEEG, LSTM, Prediction, Root Mean Square Error",

author = "Lizhe Liu and Weiting Chen and Guitao Cao",

note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 ; Conference date: 15-12-2016 Through 18-12-2016",

year = "2017",

month = jan,

day = "17",

doi = "10.1109/BIBM.2016.7822568",

language = "英语",

series = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "497--500",

editor = "Kevin Burrage and Qian Zhu and Yunlong Liu and Tianhai Tian and Yadong Wang and Hu, \{Xiaohua Tony\} and Qinghua Jiang and Jiangning Song and Shinichi Morishita and Kevin Burrage and Guohua Wang",

booktitle = "Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016",

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}

Liu, L, Chen, W & Cao, G 2017, Prediction of neonatal amplitude-integrated EEG based on LSTM method. in K Burrage, Q Zhu, Y Liu, T Tian, Y Wang, XT Hu, Q Jiang, J Song, S Morishita, K Burrage & G Wang (eds), Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016., 7822568, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Institute of Electrical and Electronics Engineers Inc., pp. 497-500, 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016, Shenzhen, China, 15/12/16. https://doi.org/10.1109/BIBM.2016.7822568

Prediction of neonatal amplitude-integrated EEG based on LSTM method. / Liu, Lizhe; Chen, Weiting; Cao, Guitao.
Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. ed. / Kevin Burrage; Qian Zhu; Yunlong Liu; Tianhai Tian; Yadong Wang; Xiaohua Tony Hu; Qinghua Jiang; Jiangning Song; Shinichi Morishita; Kevin Burrage; Guohua Wang. Institute of Electrical and Electronics Engineers Inc., 2017. p. 497-500 7822568 (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Prediction of neonatal amplitude-integrated EEG based on LSTM method

AU - Liu, Lizhe

AU - Chen, Weiting

AU - Cao, Guitao

PY - 2017/1/17

Y1 - 2017/1/17

N2 - 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.

AB - 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.

KW - AEEG

KW - LSTM

KW - Prediction

KW - Root Mean Square Error

UR - https://www.scopus.com/pages/publications/85013271844

U2 - 10.1109/BIBM.2016.7822568

DO - 10.1109/BIBM.2016.7822568

M3 - 会议稿件

AN - SCOPUS:85013271844

T3 - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

SP - 497

EP - 500

BT - Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

A2 - Burrage, Kevin

A2 - Zhu, Qian

A2 - Liu, Yunlong

A2 - Tian, Tianhai

A2 - Wang, Yadong

A2 - Hu, Xiaohua Tony

A2 - Jiang, Qinghua

A2 - Song, Jiangning

A2 - Morishita, Shinichi

A2 - Burrage, Kevin

A2 - Wang, Guohua

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016

Y2 - 15 December 2016 through 18 December 2016

ER -

Liu L, Chen W, Cao G. Prediction of neonatal amplitude-integrated EEG based on LSTM method. In Burrage K, Zhu Q, Liu Y, Tian T, Wang Y, Hu XT, Jiang Q, Song J, Morishita S, Burrage K, Wang G, editors, Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016. Institute of Electrical and Electronics Engineers Inc. 2017. p. 497-500. 7822568. (Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016). doi: 10.1109/BIBM.2016.7822568

Prediction of neonatal amplitude-integrated EEG based on LSTM method

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