Research Article ConvolutionalNeuralNetworkforSeizureDetectionofNocturnal Frontal Lobe Epilepsy Fabio Pisano , 1 Giuliana Sias , 1 Alessandra Fanni , 1 Barbara Cannas, 1 Ant´ onio Dourado , 2 Barbara Pisano, 1 and Cesar A. Teixeira 2 1 Department of Electrical and Electronic Engineering, University of Cagliari, Cagliari 09123, Italy 2 Univ Coimbra, CISUC-Center for Informatics and Systems of the University of Coimbra, Department of Informatics Engineering, Coimbra, Portugal Correspondence should be addressed to Fabio Pisano; fabio.pisano@diee.unica.it Received 9 September 2019; Accepted 22 February 2020; Published 31 March 2020 Guest Editor: Murari Andrea Copyright © 2020 Fabio Pisano et al. is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. e 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. e performances, in terms of accuracy, sensitivity, and specificity, exceed by several percentage points those in the most recent literature. e 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. is contribution aims to alleviate the task of neurologists, who may have a robust indication to corroborate their clinical conclusions. 1. Introduction Nocturnal frontal lobe epilepsy (NFLE) is a rare form of epilepsy, typically inherited, which affects both sexes from 1 to 60 years, and is associated with cognitive decline. Crises are characterized by frequent and brief hypermotor sleep seizures, which may range from a simple awakening from sleep to more focused motor movements, with dystonic and tonic postures, grimaces, screams or groans, episodic noc- turnal wanderings, and stereotyped agitated somnambulism, among others [1, 2]. In this paper, the problem of detecting NFLE seizures, using Electroencephalographic (EEG) signals as inputs to an original Convolutional Neural Network (CNN) prediction model, is addressed. e use of EEG signals is corroborated by most of the literature on epileptic seizure detection, which considers indeed the EEG the gold standard to analyse the electrical activity of the brain [3]. In the literature, there is a wide range of proposals for the identification of general epileptic seizures [4–13], mostly based on the machine-learning approach, which support the doctors in the time-consuming manual labelling [4]. However, very few contributions specifically dealing with NFLE are present, and they reach lower performance in- dices. In [14], a detection system for NFLE seizures has been proposed, which is based on accelerometer signals, obtaining a value of sensitivity of 91.67% and specificity of 84.19% on three pediatric patients. e accelerometers were already proposed in [15, 16] to detect epileptic seizures with pros and cons; even if they are more comfortable to wear, they can only reveal seizures associated to motor activity. Moreover, high false-positive rates are often possible due to motion Hindawi Complexity Volume 2020, Article ID 4825767, 10 pages https://doi.org/10.1155/2020/4825767