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Personalized Seizure Detection Using Spiking Neural Networks

Erickson, Xavante ; Bastani, Saeed LU and Aminifar, Amir LU orcid (2023) 2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023
Abstract

Around 50 million people worldwide suffer from Epilepsy, making it one of the most common neurological diseases. Epilepsy is characterized by sudden intermittent seizures, often imposing profound social and physical limitations. More importantly, the risk of premature death in these patients is up to three times that of the corresponding healthy population. It is estimated that 30% of the population with epilepsy is at risk of severe trauma, or premature death, despite currently available treatments. Smart wearable systems could mitigate many of the risks associated with epilepsy and seizures by providing early warnings for the patients and caretakers to take precautions. However, wearable systems are highly constrained in terms of... (More)

Around 50 million people worldwide suffer from Epilepsy, making it one of the most common neurological diseases. Epilepsy is characterized by sudden intermittent seizures, often imposing profound social and physical limitations. More importantly, the risk of premature death in these patients is up to three times that of the corresponding healthy population. It is estimated that 30% of the population with epilepsy is at risk of severe trauma, or premature death, despite currently available treatments. Smart wearable systems could mitigate many of the risks associated with epilepsy and seizures by providing early warnings for the patients and caretakers to take precautions. However, wearable systems are highly constrained in terms of resources and, therefore, are generally unable to utilize modern machine learning, due to their limited computing power, memory storage, and energy/battery budget. To address this issue, in this paper, we consider personalized seizure detection by adopting spiking neural networks, which are known to be efficient in terms of energy. Our experimental results demonstrate that our personalized spiking neural networks are on par with their artificial neural network counterparts in terms of performance, reaching a sensitivity of 78.8 % and a specificity of 76.9 %.

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Please use this url to cite or link to this publication:
author
; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
EEG, epilepsy, personalized seizure detection, SNN, spiking neural network
host publication
2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023
conference location
Berlin, Germany
conference dates
2023-07-23 - 2023-07-25
external identifiers
  • scopus:85167867237
ISBN
9798350346473
DOI
10.1109/COINS57856.2023.10189269
language
English
LU publication?
yes
additional info
Publisher Copyright: © 2023 IEEE.
id
d7642f0d-ea50-4ee1-bb8e-816d33e96627
date added to LUP
2023-09-16 23:09:36
date last changed
2023-11-21 22:47:31
@inproceedings{d7642f0d-ea50-4ee1-bb8e-816d33e96627,
  abstract     = {{<p>Around 50 million people worldwide suffer from Epilepsy, making it one of the most common neurological diseases. Epilepsy is characterized by sudden intermittent seizures, often imposing profound social and physical limitations. More importantly, the risk of premature death in these patients is up to three times that of the corresponding healthy population. It is estimated that 30% of the population with epilepsy is at risk of severe trauma, or premature death, despite currently available treatments. Smart wearable systems could mitigate many of the risks associated with epilepsy and seizures by providing early warnings for the patients and caretakers to take precautions. However, wearable systems are highly constrained in terms of resources and, therefore, are generally unable to utilize modern machine learning, due to their limited computing power, memory storage, and energy/battery budget. To address this issue, in this paper, we consider personalized seizure detection by adopting spiking neural networks, which are known to be efficient in terms of energy. Our experimental results demonstrate that our personalized spiking neural networks are on par with their artificial neural network counterparts in terms of performance, reaching a sensitivity of 78.8 % and a specificity of 76.9 %.</p>}},
  author       = {{Erickson, Xavante and Bastani, Saeed and Aminifar, Amir}},
  booktitle    = {{2023 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2023}},
  isbn         = {{9798350346473}},
  keywords     = {{EEG; epilepsy; personalized seizure detection; SNN; spiking neural network}},
  language     = {{eng}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Personalized Seizure Detection Using Spiking Neural Networks}},
  url          = {{http://dx.doi.org/10.1109/COINS57856.2023.10189269}},
  doi          = {{10.1109/COINS57856.2023.10189269}},
  year         = {{2023}},
}