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

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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.

Original languageEnglish
Title of host publicationProceedings of the Australasian Computer Science Week Multiconference 2020, ACSW 2020
Editors Abdur Forkan
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages8
ISBN (Electronic)9781450376976
DOIs
Publication statusPublished - 4 Feb 2020
EventAustralasian Computer Science Week Multiconference 2020 - Melbourne, Australia
Duration: 3 Feb 20207 Feb 2020
http://www.acsw.org.au/

Conference

ConferenceAustralasian Computer Science Week Multiconference 2020
Abbreviated titleACSW 2020
CountryAustralia
CityMelbourne
Period3/02/207/02/20
Internet address

Keywords

  • CNN
  • Epilepsy
  • Multi-Biosignal
  • Multimodal learning
  • Seizure detection

Cite this

Liu, Y., Sivathamboo, S., Goodin, P., Bonnington, P., Kwan, P., Kuhlmann, L., O'Brien, T., Perucca, P., & Ge, Z. (2020). Epileptic Seizure Detection Using Convolutional Neural Network: A Multi-Biosignal study. In A. Forkan (Ed.), Proceedings of the Australasian Computer Science Week Multiconference 2020, ACSW 2020 [37] Association for Computing Machinery (ACM). https://doi.org/10.1145/3373017.3373055