Analysis for Early Seizure Detection System Based on Deep Learning Algorithm

Fuxu Wang; Mingrui Sun; Tengfei Min; Yueying Wang; Chunpu Liu; Tianyi Zang; Yadong Wang

doi:10.1109/BIBM.2018.8621089

Analysis for Early Seizure Detection System Based on Deep Learning Algorithm

Fuxu Wang
, Mingrui Sun
, Tengfei Min
, Yueying Wang
, Chunpu Liu
, Tianyi Zang^*
, Yadong Wang

^*Corresponding author for this work

School of Computer Science and Technology, Harbin Institute of Technology

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

3 Scopus citations

Abstract

In recent years, deep learning technologies has developed rapidly and applies to different fields increasingly [1]. Based on the development of deep learning, Clinical Decision Support System(CDSS) is also developing rapidly. Clinical decision support systems (CDSS) can effectively reduce the misdiagnosis and missed diagnosis rate of doctors. In this situation, we can analyze the results for early seizure detection system based on deep learning algorithm to help doctors to diagnose.In this paper, the problems of seizure prediction based on electroencephalogram (EEG) dataset are studied in depth, especially the probabilistic problem of predicting pre-seizure fragments. We propose to transform time domain EEG data into frequency domain information by discrete Fourier transform. We also propose a method for extracting frequency domain and time series data features based on two-layer convolutional neural network (CNN). This method AUC index can reach 0.79. In addition, we also carry out the comparative analysis and systematic analysis of linear discriminant analysis (LDA), logistic regression (LR) and basic recurrent neural network (RNN) methods. The experimental results show that the prediction performance based on the CNN model is better than other models.

Original language	English
Title of host publication	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Editors	Harald Schmidt, David Griol, Haiying Wang, Jan Baumbach, Huiru Zheng, Zoraida Callejas, Xiaohua Hu, Julie Dickerson, Le Zhang
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	2382-2389
Number of pages	8
ISBN (Electronic)	9781538654880
DOIs	https://doi.org/10.1109/BIBM.2018.8621089
State	Published - 21 Jan 2019
Externally published	Yes
Event	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 - Madrid, Spain Duration: 3 Dec 2018 → 6 Dec 2018

Publication series

Name	Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Conference

Conference	2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018
Country/Territory	Spain
City	Madrid
Period	3/12/18 → 6/12/18

Keywords

CDSS
CNN
EEG
LDA
LR
RNN

Access to Document

10.1109/BIBM.2018.8621089

Cite this

Wang, F., Sun, M., Min, T., Wang, Y., Liu, C., Zang, T., & Wang, Y. (2019). Analysis for Early Seizure Detection System Based on Deep Learning Algorithm. In H. Schmidt, D. Griol, H. Wang, J. Baumbach, H. Zheng, Z. Callejas, X. Hu, J. Dickerson, & L. Zhang (Eds.), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 (pp. 2382-2389). Article 8621089 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2018.8621089

Wang, Fuxu ; Sun, Mingrui ; Min, Tengfei et al. / Analysis for Early Seizure Detection System Based on Deep Learning Algorithm. Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. editor / Harald Schmidt ; David Griol ; Haiying Wang ; Jan Baumbach ; Huiru Zheng ; Zoraida Callejas ; Xiaohua Hu ; Julie Dickerson ; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. pp. 2382-2389 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

@inproceedings{01222f02355f48bd8b92429a4b6f5708,

title = "Analysis for Early Seizure Detection System Based on Deep Learning Algorithm",

abstract = "In recent years, deep learning technologies has developed rapidly and applies to different fields increasingly [1]. Based on the development of deep learning, Clinical Decision Support System(CDSS) is also developing rapidly. Clinical decision support systems (CDSS) can effectively reduce the misdiagnosis and missed diagnosis rate of doctors. In this situation, we can analyze the results for early seizure detection system based on deep learning algorithm to help doctors to diagnose.In this paper, the problems of seizure prediction based on electroencephalogram (EEG) dataset are studied in depth, especially the probabilistic problem of predicting pre-seizure fragments. We propose to transform time domain EEG data into frequency domain information by discrete Fourier transform. We also propose a method for extracting frequency domain and time series data features based on two-layer convolutional neural network (CNN). This method AUC index can reach 0.79. In addition, we also carry out the comparative analysis and systematic analysis of linear discriminant analysis (LDA), logistic regression (LR) and basic recurrent neural network (RNN) methods. The experimental results show that the prediction performance based on the CNN model is better than other models.",

keywords = "CDSS, CNN, EEG, LDA, LR, RNN",

author = "Fuxu Wang and Mingrui Sun and Tengfei Min and Yueying Wang and Chunpu Liu and Tianyi Zang and Yadong Wang",

note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018 ; Conference date: 03-12-2018 Through 06-12-2018",

year = "2019",

month = jan,

day = "21",

doi = "10.1109/BIBM.2018.8621089",

language = "英语",

series = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

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

pages = "2382--2389",

editor = "Harald Schmidt and David Griol and Haiying Wang and Jan Baumbach and Huiru Zheng and Zoraida Callejas and Xiaohua Hu and Julie Dickerson and Le Zhang",

booktitle = "Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018",

address = "美国",

}

Wang, F, Sun, M, Min, T, Wang, Y, Liu, C, Zang, T & Wang, Y 2019, Analysis for Early Seizure Detection System Based on Deep Learning Algorithm. in H Schmidt, D Griol, H Wang, J Baumbach, H Zheng, Z Callejas, X Hu, J Dickerson & L Zhang (eds), Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018., 8621089, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Institute of Electrical and Electronics Engineers Inc., pp. 2382-2389, 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018, Madrid, Spain, 3/12/18. https://doi.org/10.1109/BIBM.2018.8621089

Analysis for Early Seizure Detection System Based on Deep Learning Algorithm. / Wang, Fuxu; Sun, Mingrui; Min, Tengfei et al.
Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. ed. / Harald Schmidt; David Griol; Haiying Wang; Jan Baumbach; Huiru Zheng; Zoraida Callejas; Xiaohua Hu; Julie Dickerson; Le Zhang. Institute of Electrical and Electronics Engineers Inc., 2019. p. 2382-2389 8621089 (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018).

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

TY - GEN

T1 - Analysis for Early Seizure Detection System Based on Deep Learning Algorithm

AU - Wang, Fuxu

AU - Sun, Mingrui

AU - Min, Tengfei

AU - Wang, Yueying

AU - Liu, Chunpu

AU - Zang, Tianyi

AU - Wang, Yadong

PY - 2019/1/21

Y1 - 2019/1/21

N2 - In recent years, deep learning technologies has developed rapidly and applies to different fields increasingly [1]. Based on the development of deep learning, Clinical Decision Support System(CDSS) is also developing rapidly. Clinical decision support systems (CDSS) can effectively reduce the misdiagnosis and missed diagnosis rate of doctors. In this situation, we can analyze the results for early seizure detection system based on deep learning algorithm to help doctors to diagnose.In this paper, the problems of seizure prediction based on electroencephalogram (EEG) dataset are studied in depth, especially the probabilistic problem of predicting pre-seizure fragments. We propose to transform time domain EEG data into frequency domain information by discrete Fourier transform. We also propose a method for extracting frequency domain and time series data features based on two-layer convolutional neural network (CNN). This method AUC index can reach 0.79. In addition, we also carry out the comparative analysis and systematic analysis of linear discriminant analysis (LDA), logistic regression (LR) and basic recurrent neural network (RNN) methods. The experimental results show that the prediction performance based on the CNN model is better than other models.

AB - In recent years, deep learning technologies has developed rapidly and applies to different fields increasingly [1]. Based on the development of deep learning, Clinical Decision Support System(CDSS) is also developing rapidly. Clinical decision support systems (CDSS) can effectively reduce the misdiagnosis and missed diagnosis rate of doctors. In this situation, we can analyze the results for early seizure detection system based on deep learning algorithm to help doctors to diagnose.In this paper, the problems of seizure prediction based on electroencephalogram (EEG) dataset are studied in depth, especially the probabilistic problem of predicting pre-seizure fragments. We propose to transform time domain EEG data into frequency domain information by discrete Fourier transform. We also propose a method for extracting frequency domain and time series data features based on two-layer convolutional neural network (CNN). This method AUC index can reach 0.79. In addition, we also carry out the comparative analysis and systematic analysis of linear discriminant analysis (LDA), logistic regression (LR) and basic recurrent neural network (RNN) methods. The experimental results show that the prediction performance based on the CNN model is better than other models.

KW - CDSS

KW - CNN

KW - EEG

KW - LDA

KW - LR

KW - RNN

UR - https://www.scopus.com/pages/publications/85062545471

U2 - 10.1109/BIBM.2018.8621089

DO - 10.1109/BIBM.2018.8621089

M3 - 会议稿件

AN - SCOPUS:85062545471

T3 - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

SP - 2382

EP - 2389

BT - Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

A2 - Schmidt, Harald

A2 - Griol, David

A2 - Wang, Haiying

A2 - Baumbach, Jan

A2 - Zheng, Huiru

A2 - Callejas, Zoraida

A2 - Hu, Xiaohua

A2 - Dickerson, Julie

A2 - Zhang, Le

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018

Y2 - 3 December 2018 through 6 December 2018

ER -

Wang F, Sun M, Min T, Wang Y, Liu C, Zang T et al. Analysis for Early Seizure Detection System Based on Deep Learning Algorithm. In Schmidt H, Griol D, Wang H, Baumbach J, Zheng H, Callejas Z, Hu X, Dickerson J, Zhang L, editors, Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018. Institute of Electrical and Electronics Engineers Inc. 2019. p. 2382-2389. 8621089. (Proceedings - 2018 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2018). doi: 10.1109/BIBM.2018.8621089

Analysis for Early Seizure Detection System Based on Deep Learning Algorithm

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