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Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

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Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation

dc.contributor.author	Francés Villora, José Vicente
dc.contributor.author	Bataller Mompean, Manuel
dc.contributor.author	Mjahad, Azeddine
dc.contributor.author	Rosado Muñoz, Alfredo
dc.contributor.author	Gutiérrez Martín, Antonio
dc.contributor.author	Teruel Martí, Vicent
dc.contributor.author	Villanueva, Vicente
dc.contributor.author	Hampel, Kevin G.
dc.contributor.author	Guerrero Martínez, Juan Francisco
dc.date.accessioned	2020-09-24T14:44:19Z
dc.date.available	2020-09-24T14:44:19Z
dc.date.issued	2020
dc.identifier.citation	Francés Villora, José Vicente Bataller Mompean, Manuel Mjahad, Azeddine Rosado Muñoz, Alfredo Gutiérrez Martín, Antonio Teruel Martí, Vicent Villanueva, Vicente Hampel, Kevin G. Guerrero Martínez, Juan Francisco 2020 Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation Applied Sciences-Basel 10 827
dc.identifier.uri	https://hdl.handle.net/10550/75650
dc.description.abstract	The epileptogenic focus is a brain area that may be surgically removed to control of epileptic seizures. Locating it is an essential and crucial step prior to the surgical treatment. However, given the difficulty of determining the localization of this brain region responsible of the initial seizure discharge, many works have proposed machine learning methods for the automatic classification of focal and non-focal electroencephalographic (EEG) signals. These works use automatic classification as an analysis tool for helping neurosurgeons to identify focal areas off-line, out of surgery, during the processing of the huge amount of information collected during several days of patient monitoring. In turn, this paper proposes an automatic classification procedure capable of assisting neurosurgeons online, during the resective epilepsy surgery, to refine the localization of the epileptogenic area to be resected, if they have doubts. This goal requires a real-time implementation with as low a computational cost as possible. For that reason, this work proposes both a feature set and a classifier model that minimizes the computational load while preserving the classification accuracy at 95.5%, a level similar to previous works. In addition, the classification procedure has been implemented on a FPGA device to determine its resource needs and throughput. Thus, it can be concluded that such a device can embed the whole classification process, from accepting raw signals to the delivery of the classification results in a cost-effective Xilinx Spartan-6 FPGA device. This real-time implementation begins providing results after a 5 s latency, and later, can deliver floating-point classification results at 3.5 Hz rate, using overlapped time-windows.
dc.language.iso	eng
dc.relation.ispartof	Applied Sciences-Basel, 2020, vol. 10, num. 827
dc.subject	Enginyeria biomèdica
dc.subject	Electroencefalografia
dc.subject	Electrodiagnòstic
dc.title	Real-Time Localization of Epileptogenic Foci EEG Signals: An FPGA-Based Implementation
dc.type	journal article	es_ES
dc.date.updated	2020-09-24T14:44:20Z
dc.identifier.doi	10.3390/app10030827
dc.identifier.idgrec	140457
dc.rights.accessRights	open access	es_ES