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research-article

Seizure Detection Based on EEG Signals Using Asymmetrical Back Propagation Neural Network Method

Authors:

P. BalasubramanieAuthors Info & Claims

Circuits, Systems, and Signal Processing, Volume 40, Issue 9

Pages 4614 - 4632

https://doi.org/10.1007/s00034-021-01686-w

Published: 01 September 2021 Publication History

Abstract

Abnormal activity in the human brain is a symptom of epilepsy. Electroencephalogram (EEG) is a standard tool that has been widely used to detect seizures. A number of automated seizure detection systems based on EEG signal classification have been employed in present days, which includes a mixture of approaches but most of them rely on time signal features, time intervals or time frequency domains. Therefore, in this research, deep learning-based automated mechanism is introduced to improve the seizure detection accuracy from EEG signal using the Asymmetrical Back Propagation Neural Network (ABPN) method. The ABPN system includes four levels of repetitive training with weight adjustment, feed forward initialization, error and update weight and bias back-propagation. The proposed ABPN-based seizure detection system is validated using Physionet EEG dataset with matlab simulation, and the effectiveness of proposed seizure system is confirmed through simulation results. As compared with Deep Convolutional Neural Network (CNN) and Support Vector Machine–Particle Swarm Optimization (SVM-PSO)-based seizure detection system, the proposed ABPN system gives the best performance against various parameters. The sensitivity, specificity and accuracy are 96.32%, 95.12% and 98.36%, respectively.

References

[1]

S. Abdul, P. Vir, P. Sujit, H. Leanne, B. Danushka, B. Sarah, C. Mark, fNIRS: a new modality for brain activity-based biometric authentication. (2015).

[2]

Acharya UR, ShuLih O, Hagiwara Y, and Hojjat A Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals Comput. Biol. Med. 2017

[3]

Agustina G, Lorena O, Pablo D, and Eric L Adaptive filtering for epileptic event detection in the EEG J. Med. Biol. Eng. 2019

[4]

Alexander K, Tomas S, Andreas J, and Wolfgang S Conventional and wavelet coherence applied to sensory-evoked electrical brain activity I.E.E.E. Trans. Biomed. Eng. 2006 53 266-272

[5]

Ali E, Naoto K, Takeshi M, Takashi S, Kensuke K, and Hirokazu T Seizure detection by convolutional neural network-based analysis of scalp electroencephalography plot images NeuroImage Clin. 2019 22 101684

[6]

K. Amarasinghe, D. Wijayasekara, M. Milos, EEG based brain activity monitoring using Artificial Neural Networks. Proceedings—2014 7th International Conference on Human System Interactions, HSI 2014. (2014).

[7]

Andrew M and Tom C A passive EEG-BCI for single-trial detection of changes in mental state IEEE Trans. Neural Syst. Rehabil. Eng. 2017

[8]

B. Bartosz, G. Tomasz, N. Michał, Normalization of feature distribution in motor imagery based brain-computer interfaces. 1337–1342 (2016).

[9]

K.D. Brabanter, P. Karsmakers, F. Ojeda, C. Alzate, J.D Brabanter, K. Pelckmans, Moor, B.D.; Vandewalle, J. Suykens, J.A.K. LS-SVMlab Toolbox User’s Guide: Version 1.7; Ku Leuven Leuven: Leuven, Belgium, (2010)

[10]

D. Chen, S. Wan, F.S. Bao, Epileptic focus localization using EEG based on discrete wavelet transform through full-level decomposition, in 2015 IEEE 25th International Workshop on Machine Learning for Signal Processing (MLSP), (2015), pp. 1–6

[11]

Chicco D and Jurman G The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation BMC Genomics 2020 21 6

[12]

M. Dong, Z. Yuhuan, Q. Hongzhi, W. Baikun, H. Yong, K.D.K. Luk, Study on EEG-based mouse system by using brain-computer interface. 236–239 (2009).

[13]

S. K. EhsanKamrani, S.H. Andy Yun, Optical EEG (OEEG): A novel technique toward plug and-play non-invasive brain imaging and human-machine interfacing. IEEE Photonics Conference (IPC), pp. 433–433, Conference location. USA (2015)

[14]

O. Elma, A Paradigm for epileptic seizure prediction using a coupled oscillator model of the brain. Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp.6428–6431, Conference location. USA, (2009)

[15]

Fu K and Qu J Hilbert marginal spectrum analysis for automatic seizure detection in EEG signals Biomed. Signal Process. Control 2015 18 179-185

[16]

V. Gonuguntla, R. Mallipeddi, K.C. Veluvolu, Identification of instinct associated brain functional network with phase locking value. International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (2016)

[17]

Guo L, Rivero D, Dorado J, Rabunal JR, and Pazos A Automatic epileptic seizure detection in EEGs based on line length feature and artificial neural networks J. Neurosci. Methods 2010 191 1 101-109

[18]

L.D. Guofashou, EEG-based single-trail detection of errors from multiple error-related brain activity. IEEE International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC),pp. 2764–2767,Conference location. USA. (2016)

[19]

Ihsan U, Muhammad H, Emad-ul-Haq Q, and Hatim A An automated system for epilepsy detection using EEG brain signals based on deep learning approach Exp. Syst. Appl. 2018

[20]

C. Jee Hyun, L. Minah, K. Jong-ho, S. Hee-Sup, A flexible multichannel electrode for mouse brain and its application to mouse EEG. 230–231 (2007).

[21]

C. JiantingCao, D. Watanabe, L. Zhang, An EEG diagnosis system for quasi-brain-death based on complexity and energy analyses. Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp.7132–7135, Conference location. Japan (2013)

[22]

Jiao Y and Du P Performance measures in evaluating machine learning based bioinformatics predictors for classifications Quant. Biol. 2016 4 320-330

[23]

Z. Khalili, M. H. Moradi, Emotion detection using brain and peripheral signals, in International Biomedical Engineering Conference, (2008)

[24]

Lee S-H and Lim JS Minimum feature selection for epileptic seizure classification using wavelet-based feature extraction and a fuzzy neural network Appl. Math. Inf. Sci. 2014 8 3 1295

[25]

Li M, Chen W, and Zhang T Automatic epilepsy detection using wavelet-based nonlinear analysis and optimized SVM Biocybern. Biomed. Eng. 2016 36 4 708-718

[26]

Li P, Karmakar C, Yan C, Palaniswami M, and Liu C Classification of five-second epileptic EEG recordings using distribution entropy and sample entropy Front Physiol. 2016 7 136 PMID: 27148074

[27]

Lima CA, Coelho AL, and Eisencraft M Tackling EEG signal classification with least squares support vector machines: a sensitivity analysis study Comput. Biol. Med. 2010 40 8 705-714

[28]

Masaki N, Yijun W, and Xiaogang C Enhancing detection of ssveps for a high-speed brain speller using task-related component analysis IEEE Trans. Biomed. Eng. 2018 65 104-112

[29]

Mengni Z, Cheng T, Cao R, Bin W, Yan N, Ting H, Hao G, and Jie X Epileptic seizure detection based on EEG signals and CNN Front. Neuroinform 2018

[30]

G. Molina, T. Tsvetomira, A. Nijholt, Instinctal Brain-Computer Interfaces. IEEE International Conference on Affective Computing and Intelligent Interaction and Workshops, (2009)

[31]

Mousavi S, Afghah F, and Acharya UR SleepEEGNet: automated sleep stage scoring with sequence to sequence deep learning approach PLoS ONE 2019 14 5 e0216456

[32]

Nagabushanam P, George ST, and Radha S EEG signal classification using LSTM and improved neural network algorithms Soft Comput. 2020 24 9981-10003

[33]

S. NoufalSulthan, N. Mohan, K. Ahmad Khan, Instinct recognition using brain signals, in 2018 Int. Conference on Intelligent Circuits and Systems (ICICS), (2018)

[34]

I. Omerhodzic, S. Avdakovic, A. Nuhanovic, K. Dizdarevic, Energy distribution of EEG signals: EEG signal wavelet-neural network classifier. arXiv preprint arXiv:1307.7897, (2013)

[35]

Ozan K and Reza L Classification of EEG signals for epileptic seizures using hybrid artificial neural networks based wavelet transforms and fuzzy relations Exp. Syst. Appl. 2017

[36]

Panagiotis C and Petrantonakis L A novel instinct elicitation index using frontal brain asymmetry for enhanced EEG-based instinct recognition IEEE Trans. Inf. Technol. Biomed. 2011 15 5 2011

[37]

S. Ritu Ranjan, P. Vikramkumar, C. Anupam, An FPGA-based brain computer interfacing using compressive sensing and machine learning. 726–731 (2018).

[38]

Shouyi W, Yiqi Z, Changxu W, Felix D, and Wanpracha C Online prediction of driver distraction based on brain activity patterns Intell. Transp. Syst. IEEE Trans. 2014

[39]

Sreeja SR, Rabha J, Samanta D, Mitra P, and Sarma M Horain P, Achard C, and Mallem M Classification of motor imagery based EEG signals using sparsity approach Intelligent Human Computer Interaction IHCI 2017 Lecture Notes in Computer Science 2017 Cham Springer

[40]

Sreeja SR and Samanta D Distance-based weighted sparse representation to classify motor imagery EEG signals for BCI applications Multimed. Tools Appl. 2020 79 13775-13793

[41]

Subasi A EEG signal classification using wavelet feature extraction and a mixture of expert model Expert Syst. Appl. 2007 32 4 1084-1093

[42]

Tong S and Thakor NV Time-frequency complexity of EEG following hypoxic-ischemic brain injury Int. Conf. IEEE Eng. Med. Biol. Soc. 2003 3 2570-2573

[43]

L. Trieu Phat, H. Yongtian, N. Sho, J. Contreras-Vidal,. EEG-based brain-computer interface to a virtual walking avatar engages cortical adaptation. (2017)

Cited By

Sharma RMeena H(2024)Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG SignalsCircuits, Systems, and Signal Processing10.1007/s00034-024-02715-043:8(5288-5308)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00034-024-02715-0
Moghadam AKarami Mollaei MHassanzadeh M(2024)A Signal-Based One-Dimensional Convolutional Neural Network (SB 1D CNN) Model for Seizure PredictionCircuits, Systems, and Signal Processing10.1007/s00034-024-02700-743:8(5211-5236)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00034-024-02700-7
Deb SWarule PNair ASultan HDash RKrajewski J(2023)Detection of Common Cold from Speech Signals using Deep Neural NetworkCircuits, Systems, and Signal Processing10.1007/s00034-022-02189-y42:3(1707-1722)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s00034-022-02189-y

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Seizure Detection Based on EEG Signals Using Asymmetrical Back Propagation Neural Network Method

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Information & Contributors

Information

Published In

cover image Circuits, Systems, and Signal Processing

Circuits, Systems, and Signal Processing Volume 40, Issue 9

Sep 2021

533 pages

ISSN:0278-081X

Issue’s Table of Contents

© The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2021.

Publisher

Birkhauser Boston Inc.

United States

Publication History

Published: 01 September 2021

Accepted: 19 February 2021

Revision received: 17 February 2021

Received: 09 May 2020

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Cited By

Sharma RMeena H(2024)Enhanced Epileptic Seizure Detection Through Graph Spectral Analysis of EEG SignalsCircuits, Systems, and Signal Processing10.1007/s00034-024-02715-043:8(5288-5308)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00034-024-02715-0
Moghadam AKarami Mollaei MHassanzadeh M(2024)A Signal-Based One-Dimensional Convolutional Neural Network (SB 1D CNN) Model for Seizure PredictionCircuits, Systems, and Signal Processing10.1007/s00034-024-02700-743:8(5211-5236)Online publication date: 1-Aug-2024
https://dl.acm.org/doi/10.1007/s00034-024-02700-7
Deb SWarule PNair ASultan HDash RKrajewski J(2023)Detection of Common Cold from Speech Signals using Deep Neural NetworkCircuits, Systems, and Signal Processing10.1007/s00034-022-02189-y42:3(1707-1722)Online publication date: 1-Mar-2023
https://dl.acm.org/doi/10.1007/s00034-022-02189-y
He XJin XZheng J(2022)Research on Virtual Human Motion Control Based on Computer-Assisted Multimedia SimulationComputational Intelligence and Neuroscience10.1155/2022/19022072022Online publication date: 1-Jan-2022
https://dl.acm.org/doi/10.1155/2022/1902207

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