Titolo:  Convolutional Neural Network for Seizure Detection of Nocturnal Frontal Lobe Epilepsy
Data di pubblicazione:  2020
Autori:  Pisano, F.; Sias, G.; Fanni, A.; Cannas, B.; Dourado, A.; Pisano, B.; Teixeira, C. A.
Numero degli autori:  7
Lingua:  Inglese
Presenza coautori internazionali: 
Volume:  2020
Fascicolo:  Special Issue: Frontiers in Data-Driven Methods for Understanding, Prediction, and Control of Complex Systems
Numero dell’articolo:  4825767
Pagina iniziale:  1
Pagina finale:  10
Numero di pagine:  10
Digital Object Identifier (DOI):  http://dx.doi.org/10.1155/2020/4825767
Codice identificativo Scopus:  2-s2.0-85083628146
Codice identificativo ISI:  WOS:000526861700006
URL:  https://www.hindawi.com/journals/complexity/2020/4825767/
Abstract:  The Nocturnal Frontal Lobe Epilepsy (NFLE) is a form of epilepsy in which seizures occur predominantly during sleep. In other forms of epilepsy, the commonly used clinical approach mainly involves manual inspection of encephalography (EEG) signals, a laborious and time-consuming process which often requires the contribution of more than one experienced neurologist. In the last decades, numerous approaches to automate this detection have been proposed and, more recently, machine learning has shown very promising performance. In this paper, an original Convolutional Neural Network (CNN) architecture is proposed to develop patient-specific seizure detection models for three patients affected by NFLE. The performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. The capability of the patient-specific models has been also tested to compare the obtained seizure onset times with those provided by the neurologists, with encouraging results. Moreover, the same CNN architecture has been used to develop a cross-patient seizure detection system, resorting to the transfer-learning paradigm. Starting from a patient-specific model, few data from a new patient are enough to customize his model. This contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions.
Parole chiave:  Brain; Convolution; Learning systems; Network architecture; Neurology; Transfer learning
Tipologia: 1.1 Articolo in rivista

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