Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2020-04-30

AUTHORS

Pouya Soltani Zarrin , Romain Zimmer , Christian Wenger , Timothée Masquelier

ABSTRACT

Monitoring brain activities of Drug-Resistant Epileptic (DRE) patients is crucial for the effective management of the chronic epilepsy. Implementation of machine learning tools for analyzing electrical signals acquired from the cerebral cortex of DRE patients can lead to the detection of a seizure prior to its development. Therefore, the objective of this work was to develop a deep Spiking Neural Network (SNN) for the epileptic seizure detection. The energy and computation-efficient SNNs are well compatible with neuromorphic systems, making them an adequate model for edge-computing devices such as healthcare wearables. In addition, the integration of SNNs with neuromorphic chips enables the secure analysis of sensitive medical data without cloud computations. More... »

PAGES

389-394

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-030-45385-5_34

DOI

http://dx.doi.org/10.1007/978-3-030-45385-5_34

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1127241610


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