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.
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Acknowledgement
The authors thank the department of science and technology of the French embassy in Berlin (SST) and the French national institute for research in computer science and automation (Inria) for funding this project under the AI-Procope mobility grant. The authors would also like to thank the staff at CerCo-CNRS Toulouse, especially Emmanuel Barbeau and Simon Thorpe, for their precious help with this work and IHP for its initial support with the mobility program.
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Zarrin, P.S., Zimmer, R., Wenger, C., Masquelier, T. (2020). Epileptic Seizure Detection Using a Neuromorphic-Compatible Deep Spiking Neural Network. In: Rojas, I., Valenzuela, O., Rojas, F., Herrera, L., Ortuño, F. (eds) Bioinformatics and Biomedical Engineering. IWBBIO 2020. Lecture Notes in Computer Science(), vol 12108. Springer, Cham. https://doi.org/10.1007/978-3-030-45385-5_34
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