research-article

Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study

Authors:

Shobi Sivathamboo,

Paul Bonnington,

Levin Kuhlmann,

Terence O'Brien,

Zongyuan GeAuthors Info & Claims

ACSW '20: Proceedings of the Australasian Computer Science Week Multiconference

Article No.: 37, Pages 1 - 8

https://doi.org/10.1145/3373017.3373055

Published: 04 February 2020 Publication History

Abstract

Epilepsy affects over 70 million people worldwide, making it one of the most common serious neurological disorders in the world. The automated identification of seizures based on EEG signal is one of the most common methods but facing challenges such as the variability of seizures between individual patients and artifact generated during the measurement. In this work, we implement the multi-biosignals scheme for seizure detection by combing EEG, ECG and respiratory. We apply 1D and 2D convolutional neural network (CNN) on multi-biosignal epileptic seizure detection using the in-situ dataset with artifacts. The experimental results show that incorporating multi-biosignals outperforms than using EEG only. We also discovered that Conv2D model could achieve the best AUC of 65%, which is 7% better than the Conv1D model.

References

[1]

Fisher, R.S., ILAE Official Report: A practical clinical definition of epilepsy.Epilepsia (Series 4), 2014. 55 (4): p. 475-482.

[2]

Kwan, P. and M.J. Brodie, Early Identification of Refractory Epilepsy.New England Journal of Medicine, 2000. 342 (5): p. 314-319.

[3]

Sillanpaa, M. and S. Shinnar, Long-term mortality in childhood-onset epilepsy.N Engl J Med, 2010. 363 (26): p. 2522-9.

[4]

Devinsky, O., Sudden unexpected death in epilepsy: epidemiology, mechanisms, and prevention.Lancet Neurol, 2016. 15 (10): p. 1075-88.

[5]

Ficker, D.M., Population-based study of the incidence of sudden unexplained death in epilepsy.Neurology, 1998. 51 (5): p. 1270-4.

[6]

Thurman, D.J., D.C. Hesdorffer, and J.A. French, Sudden unexpected death in epilepsy: assessing the public health burden.Epilepsia, 2014. 55 (10): p. 1479-85.

[7]

Lamberts, R.J., Sudden unexpected death in epilepsy: people with nocturnal seizures may be at highest risk.Epilepsia, 2012. 53 (2): p. 253-7.

[8]

Ryvlin, P., Incidence and mechanisms of cardiorespiratory arrests in epilepsy monitoring units (MORTEMUS): a retrospective study.The Lancet Neurology, 2013. 12 (10): p. 966-977.

[9]

Harden, C., Practice guideline summary: Sudden unexpected death in epilepsy incidence rates and risk factors: Report of the Guideline Development, Dissemination, and Implementation Subcommittee of the American Academy of Neurology and the American Epilepsy Society.Neurology, 2017. 88 (17): p. 1674-1680.

[10]

Surges, R., C.A. Scott, and M.C. Walker, Enhanced QT shortening and persistent tachycardia after generalized seizures.Neurology, 2010. 74 (5): p. 421-426.

[11]

Hoppe, C., A. Poepel, and C.E. Elger, Epilepsy: Accuracy of patient seizure counts.Archives of Neurology, 2007. 64 (11): p. 1595-1599.

[12]

Zhang, J., Automatic detection of interictal epileptiform discharges based on time-series sequence merging method.Neurocomputing, 2013.110: p. 35-43.

[13]

Aarabi, A., R. Fazel-Rezai, and Y. Aghakhani, A fuzzy rule-based system for epileptic seizure detection in intracranial EEG.Clinical Neurophysiology, 2009. 120 (9): p. 1648-1657.

[14]

Boashash, B. and G. Azemi, A review of time–frequency matched filter design with application to seizure detection in multichannel newborn EEG.Digital Signal Processing, 2014.28: p. 28-38.

[15]

Osorio, I. and M. Frei, Real-time detection, quantification, warning, and control of epileptic seizures: The foundations for a scientific epileptology.Epilepsy & Behavior, 2009. 16 (3): p. 391-396.

[16]

Faust, O., Wavelet-based EEG processing for computer-aided seizure detection and epilepsy diagnosis.Seizure, 2015.26: p. 56-64.

[17]

Abualsaud, K., Ensemble classifier for epileptic seizure detection for imperfect EEG data.The Scientific World Journal, 2015.2015.

[18]

Asif, U., SeizureNet: A Deep Convolutional Neural Network for Accurate Seizure Type Classification and Seizure Detection.arXiv preprint arXiv:1903.03232, 2019.

[19]

Acharya, U.R., Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals.Computers in biology and medicine, 2018.100: p. 270-278.

[20]

Antoniades, A., Deep learning for epileptic intracranial EEG data. in2016 IEEE 26th International Workshop on Machine Learning for Signal Processing (MLSP). 2016. IEEE.

[21]

Hussein, R., Epileptic seizure detection: A deep learning approach.arXiv preprint arXiv:1803.09848, 2018.

[22]

Tsiouris, Κ.Μ., A Long Short-Term Memory deep learning network for the prediction of epileptic seizures using EEG signals.Computers in biology and medicine, 2018.99: p. 24-37.

[23]

Cogan, D., Multi-biosignal analysis for epileptic seizure monitoring.International journal of neural systems, 2017. 27 (01): p. 1650031.

[24]

Opherk, C., J. Coromilas, and L.J. Hirsch, Heart rate and EKG changes in 102 seizures: analysis of influencing factors.Epilepsy Res, 2002. 52 (2): p. 117-27.

[25]

Marshall, D.W., B.F. Westmoreland, and F.W. Sharbrough, Ictal tachycardia during temporal lobe seizures.Mayo Clinic Proceedings, 1983. 58 (7): p. 443-446.

[26]

Blumhardt, L.D., P.E. Smith, and L. Owen, Electrocardiographic accompaniments of temporal lobe epileptic seizures.Lancet (London, England), 1986. 1 (8489): p. 1051-1056.

[27]

Smith, P.E., Profiles of instant heart rate during partial seizures.Electroencephalogr Clin Neurophysiol, 1989. 72 (3): p. 207-17.

[28]

Leutmezer, F., Electrocardiographic changes at the onset of epileptic seizures.Epilepsia, 2003. 44 (3): p. 348-54.

[29]

Rocamora, R., Cardiac Asystole in Epilepsy: Clinical and Neurophysiologic Features.Epilepsia, 2003. 44 (2): p. 179-185.

[30]

Schuele, S.U., Video-electrographic and clinical features in patients with ictal asystole.Neurology, 2007. 69 (5): p. 434-41.

[31]

Bateman, L.M., C.-S. Li, and M. Seyal, Ictal hypoxemia in localization-related epilepsy: analysis of incidence, severity and risk factors.Brain, 2008. 131 (12): p. 3239-3245.

[32]

Nashef, L., Apnoea and bradycardia during epileptic seizures: relation to sudden death in epilepsy.Journal of Neurology, Neurosurgery, and Psychiatry, 1996. 60 (3): p. 297-300.

[33]

Osorio, I., Automated seizure detection using EKG.Int J Neural Syst, 2014. 24 (2): p. 1450001.

[34]

Ryvlin, P. and S. Beniczky, Seizure detection and mobile health devices in epilepsy: Update and future developments.Epilepsia, 2018. 59 (S1): p. 7-8.

[35]

Sivathamboo, S., Sleep-disordered breathing among patients admitted for inpatient video-EEG monitoring.Neurology, 2019.

[36]

Fisher, R.S., Operational classification of seizure types by the International League Against Epilepsy: Position Paper of the ILAE Commission for Classification and Terminology.Epilepsia, 2017. 58 (4): p. 522-530.

[37]

Berry, R.B., Rules for Scoring Respiratory Events in Sleep: Update of the 2007 AASM Manual for the Scoring of Sleep and Associated Events: Deliberations of the Sleep Apnea Definitions Task Force of the American Academy of Sleep Medicine.Journal of Clinical Sleep Medicine : JCSM : Official Publication of the American Academy of Sleep Medicine, 2012. 8 (5): p. 597-619.

[38]

Lhatoo, S.D., An electroclinical case-control study of sudden unexpected death in epilepsy.Ann Neurol, 2010. 68 (6): p. 787-96.

[39]

Bigdely-Shamlo, N., The PREP pipeline: standardized preprocessing for large-scale EEG analysis.Frontiers in neuroinformatics, 2015.9: p. 16.

[40]

Jas, M., Autoreject: Automated artifact rejection for MEG and EEG data.NeuroImage, 2017.159: p. 417-429.

[41]

Roy, Y., Deep learning-based electroencephalography analysis: a systematic review.Journal of neural engineering, 2019.

[42]

Kuhlmann, L., Seizure detection using seizure probability estimation: Comparison of features used to detect seizures.Annals of biomedical engineering, 2009. 37 (10): p. 2129-2145.

[43]

Schirrmeister, R.T., Deep learning with convolutional neural networks for EEG decoding and visualization.Human brain mapping, 2017. 38 (11): p. 5391-5420.

[44]

Zhang, X. and Y. LeCun, Text understanding from scratch.arXiv preprint arXiv:1502.01710, 2015.

[45]

He, T., Bag of tricks for image classification with convolutional neural networks. in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2019.

[46]

Ren, S., Faster r-cnn: Towards real-time object detection with region proposal networks. in Advances in neural information processing systems. 2015.

Digital Library

[47]

He, K., Mask r-cnn. in Proceedings of the IEEE international conference on computer vision. 2017.

[48]

Chollet, F.a.o. Keras. 2015; Available from: https://keras.io.

Cited By

El-Dajani NWilhelm TBaumann JSurges RMeyer B(2025)Patient-Independent Epileptic Seizure Detection with Reduced EEG Channels and Deep Recurrent Neural NetworksInformation10.3390/info1601002016:1(20)Online publication date: 3-Jan-2025
https://doi.org/10.3390/info16010020
Wong SSimmons ARivera-Villicana JBarnett SSivathamboo SPerucca PGe ZKwan PKuhlmann LO’Brien T(2025)Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detectionBiomedical Signal Processing and Control10.1016/j.bspc.2024.107484103(107484)Online publication date: May-2025
https://doi.org/10.1016/j.bspc.2024.107484
Fussner SBoyne AHan ANakhleh LHaneef Z(2024)Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot ImagesSensors10.3390/s2409282324:9(2823)Online publication date: 29-Apr-2024
https://doi.org/10.3390/s24092823
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Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study
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ACSW '20: Proceedings of the Australasian Computer Science Week Multiconference

February 2020

367 pages

ISBN:9781450376976

DOI:10.1145/3373017

Copyright © 2020 ACM.

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Published: 04 February 2020

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ACSW '20

ACSW '20: Australasian Computer Science Week 2020

February 4 - 6, 2020

VIC, Melbourne, Australia

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Cited By

El-Dajani NWilhelm TBaumann JSurges RMeyer B(2025)Patient-Independent Epileptic Seizure Detection with Reduced EEG Channels and Deep Recurrent Neural NetworksInformation10.3390/info1601002016:1(20)Online publication date: 3-Jan-2025
https://doi.org/10.3390/info16010020
Wong SSimmons ARivera-Villicana JBarnett SSivathamboo SPerucca PGe ZKwan PKuhlmann LO’Brien T(2025)Channel-annotated deep learning for enhanced interpretability in EEG-based seizure detectionBiomedical Signal Processing and Control10.1016/j.bspc.2024.107484103(107484)Online publication date: May-2025
https://doi.org/10.1016/j.bspc.2024.107484
Fussner SBoyne AHan ANakhleh LHaneef Z(2024)Differentiating Epileptic and Psychogenic Non-Epileptic Seizures Using Machine Learning Analysis of EEG Plot ImagesSensors10.3390/s2409282324:9(2823)Online publication date: 29-Apr-2024
https://doi.org/10.3390/s24092823
Alsaadan AAlzamel MHussain M(2024)LMPSeizNet: A Lightweight Multiscale Pyramid Convolutional Neural Network for Epileptic Seizure Detection on EEG Brain SignalsMathematics10.3390/math1223364812:23(3648)Online publication date: 21-Nov-2024
https://doi.org/10.3390/math12233648
Baghersalimi SAmirshahi AForooghifar FTeijeiro TAminifar AAtienza D(2024)M2SKD: Multi-to-Single Knowledge Distillation of Real-Time Epileptic Seizure Detection for Low-Power Wearable SystemsACM Transactions on Intelligent Systems and Technology10.1145/367540215:5(1-31)Online publication date: 4-Jul-2024
https://dl.acm.org/doi/10.1145/3675402
Baghersalimi STeijeiro TAminifar AAtienza D(2024)Decentralized Federated Learning for Epileptic Seizures Detection in Low-Power Wearable SystemsIEEE Transactions on Mobile Computing10.1109/TMC.2023.3320862(1-16)Online publication date: 2024
https://doi.org/10.1109/TMC.2023.3320862
Joseph LIzudheen SNarayanana UNair AK A A(2024)An Efficient Approach for EEG Seizure Detection using CNN with Feature Extraction2024 7th International Conference on Circuit Power and Computing Technologies (ICCPCT)10.1109/ICCPCT61902.2024.10673095(1924-1929)Online publication date: 8-Aug-2024
https://doi.org/10.1109/ICCPCT61902.2024.10673095
Kantipudi MKumar NAluvalu RSelvarajan SKotecha K(2024)An improved GBSO-TAENN-based EEG signal classification model for epileptic seizure detectionScientific Reports10.1038/s41598-024-51337-814:1Online publication date: 8-Jan-2024
https://doi.org/10.1038/s41598-024-51337-8
Georgis-Yap ZPopovic MKhan S(2024)Supervised and Unsupervised Deep Learning Approaches for EEG Seizure PredictionJournal of Healthcare Informatics Research10.1007/s41666-024-00160-x8:2(286-312)Online publication date: 16-Feb-2024
https://doi.org/10.1007/s41666-024-00160-x
Yang YQin XWen HLi FLin X(2023)Patient-specific approach using data fusion and adversarial training for epileptic seizure predictionFrontiers in Computational Neuroscience10.3389/fncom.2023.117298717Online publication date: 4-May-2023
https://doi.org/10.3389/fncom.2023.1172987
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