A two-layer LSTM Deep Learning model for epileptic seizure prediction

Shiva Maleki Varnosfaderani, Rihat Rahman, Nabil J. Sarhan, Levin Kuhlmann, Eishi Asano, Aimee Luat, Mohammad Alhawari

Research output: Chapter in Book/Report/Conference proceedingConference PaperResearchpeer-review

1 Citation (Scopus)

Abstract

We propose an efficient seizure prediction model based on a two-layer LSTM using the Swish activation function. The proposed structure performs feature extraction based on the time and frequency domains and uses the minimum distance algorithm as a post-processing step. The proposed model is evaluated on the Melbourne dataset and achieves the highest Area Under Curve (AUC) score of 0.92 and the lowest False Positive Rate (FPR) of 0.147 compared to previous work while having sensitivity and accuracy of 86.8 and 85.1, respectively. The proposed system has a low number of trainable parameters, and thus reducing the complexity of resource-constrained applications.

Original languageEnglish
Title of host publication2021 IEEE 3rd International Conference on Artificial Intelligence Circuits and Systems (AICAS 2021)
EditorsWujie Wen
Place of PublicationPiscataway NJ USA
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages165-168
Number of pages4
ISBN (Electronic)9781665419130
ISBN (Print)9781665430258
DOIs
Publication statusPublished - 2021
EventIEEE International Conference on Artificial Intelligence Circuits and Systems 2021 - Online, Washington, United States of America
Duration: 6 Jun 20219 Jun 2021
Conference number: 3rd
http://www.proceedings.com/59376.html (Proceedings)
http://aicas2021.org/index (Website)

Conference

ConferenceIEEE International Conference on Artificial Intelligence Circuits and Systems 2021
Abbreviated titleAICAS 2021
CountryUnited States of America
CityWashington
Period6/06/219/06/21
Internet address

Keywords

  • classification
  • Deep learning
  • epilepsy seizure prediction
  • feature extraction
  • iEEG
  • LSTM
  • Melbourne dataset

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