The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy

A Shoeb

AS Zandi

B Direito

I Arel

J Gotman

L Deng

M Hosseini

RB Yaffe

SF Liang

TS Kumar

U Acharya

Y LeCun

Y. Bengio

Bouaziz, Bassem

Chaari, Lotfi

Batatia, Hadj

Quintero Rincón, Antonio

Open Archive Toulouse Archive Ouverte

    OATAO is an open access repository that collects the work of Toulouse researchers and makes it freely available over the web where possible Any correspondence concerning this service should be sent  to the repository administrator: tech-oatao@listes-diff.inp-toulouse.fr This is a Publisher’s version published in: http://oatao.univ-toulouse.fr/24138  To cite this version:  Bouaziz, Bassem and Chaari, Lotfi and Batatia, Hadj  and Quintero Rincon, Antonio Epileptic Seizure Detection Using a Convolutional Neural Network. (2019) In: Digital Health Approach for Predictive, Preventive, Personalised and Participatory Medicine. (Advances in Predictive, Preventive and Personalised Medicine). Springer, Cham, 79-86. ISBN 978-3-030-11799-3  Official URL: https://doi.org/10.1007/978-3-030-11800-6_9  Epileptic Seizure Detection Using aConvolutional Neural NetworkBassem Bouaziz, Lotfi Chaari, Hadj Batatia, and Antonio Quintero-RincónAbstract The availability of electroencephalogram (EEG) data has opened up thepossibility for new interesting applications, such as epileptic seizure detection. Thedetection of epileptic activity is usually performed by an expert based on the analysisof the EEG data. This paper shows how a convolutional neural network (CNN)can be applied to EEG images for a full and accurate classification. The proposedmethodology was applied on images reflecting the amplitude of the EEG data overall electrodes. Two groups are considered: (a) healthy subjects and (b) epilepticsubjects. Classification results show that CNN has a potential in the classificationof EEG signals, as well as the detection of epileptic seizures by reaching 99.48% ofoverall classification accuracy.Keywords Epilepsy · CNN · Seizure detection · EEGB. Bouaziz ()MIRACL Laboratory, University of Sfax, Sfax, Tunisiae-mail: Bassem.Bouaziz@isims.usf.tnL. ChaariMIRACL Laboratory, University of Sfax, Sfax, TunisiaDigital Research Centre of Sfax, Sfax, Tunisiae-mail: Lotfi.Chaari@isims.usf.tnH. BatatiaIRIT, University of Toulouse, Toulouse, Francee-mail: hadj.batatia@inp-toulouse.frA. Quintero-RincónDepartment of Bioengineering, Instituto Tecnológico de Buenos Aires (ITBA), Buenos Aires,Argentinae-mail: aquinter@itba.edu.ar© Springer Nature Switzerland AG 2019L. Chaari (ed.), Digital Health Approach for Predictive, Preventive, Personalisedand Participatory Medicine, Advances in Predictive, Preventive and PersonalisedMedicine 10, https://doi.org/10.1007/978-3-030-11800-6_97980 B. Bouaziz et al.1 IntroductionEpilepsy, affecting more than 60 million people of the worldwide population, is anervous system disorder characterized by seizures. These seizures are commonlydefined as sudden disturbances in brain functions that indicate hyper-synchronousactivity of neurons in the brain [6, 13]. Hence, epilepsy disease often leads to lossof upright posture and impairment of the senses that can lead to serious injuries andsometimes death [6, 13, 14]. Although the main reason of this disease is unknown,most of the epileptic patients can be medically treated if seizures are diagnosed ontime. As a clinical tool, the electroencephalogram (EEG) signal is very importantin the diagnosis of epilepsy. It provides a snapshot of the human brain whileperforming a cognitive task or even at a resting state. The EEG is collected byplacing electrodes on the scalp of the patient and can then record the electro-activityproduced by the brain. Traditionally, diagnosis using EEG signals is performed byexperts based on the visual inspection to detect seizures. Such a costly and slowprocess is subject to human errors. It can also lead to ambiguous interpretationsand even disagreements between experts. Recent works achieved seizure detectionby preprocessing data collected from electrodes. This processing is followed by ahand-crafted feature extraction step based on signal properties to be analyzed andclassified later based on signal properties using a machine learning algorithm.Though deep learning algorithms [3] achieve promising results on many tasks,we present in this paper a convolutional neural network to detect seizures basedon images generated from EEG signals. The algorithm takes an intensity imagecalculated from EEG data as an input and classifies the images into epileptic andhealthy. The learning is performed on the intensity images gathering signals from allthe used electrodes. Operating on 200 epileptic and 500 healthy images, experimentsshow that our system performs 98.48 % overall accuracy.The remainder of this paper is organized as follows. An overview of existingtechniques used for seizure detection from EEG data is presented in the secondsection. The third section is dedicated to the proposed approach. Section 4 describesthe experimental validation of the proposed method. The last section is dedicated tothe conclusion and to draw the future direction of this work.2 Related WorkFor seizure detection, several approaches have been developed in the recentliterature such as those based on classification using time-frequency features [5, 10]or component analysis [7] and some others [1, 8]. Two main ideas are used formost of the developed methods. Either authors decompose the EEG signal anduse appropriate components, or they extract appropriate features from the signalto perform classification. In this sense, even if the features type is crucial for theaccuracy of seizure detection, choosing an appropriate classification algorithm isimportant too.Epileptic Seizure Detection Using a Convolutional Neural Network 81In our work, we investigated the application of convolution neural network(CNN) [1] which is a method that avoids exhaustive prepossessing data and hand-crafted feature extraction. Besides, the presence of a large amount of EEG dataand powerful computing infrastructure made CNN a suitable candidate for thecurrent application. This CNN has demonstrated its ability to provide high goodclassification rates in a number of applications such as vehicle detection [15] andmedical imaging [11].3 Proposed ApproachTo deal with the mentioned challenges, we introduce a deep learning-basedapproach to classify and identify epileptic seizure. The general architectureof the proposed framework is illustrated in Fig. 1. The figure shows that thepipeline consists of two main steps: dataset construction and convolutional neuralnetwork (CNN). The CNN contains self-taught feature extraction and classificationcomponents. In the following, we describe each step.3.1 Dataset ConstructionThis process consists of constructing input data for the CNN model in order to detectseizure. Starting from continuous EEG signals issued from annotated dataset of 23electrodes, we extract a fixed temporal window of 2 s. The obtained windows arethen transformed to a spatial representation by producing a set of intensity imagesas shown in Fig. 2b.Fig. 1 Pipeline of the proposed approach82 B. Bouaziz et al.Fig. 2 Temporal EEG signals of the 23 electrodes (a) and the spatial representation (b)3.2 CNN for Seizure DetectionConvolutional neural networks (CNNs) are a family of multilayer neural networksand are considered as the first successful trial for deep learning approach wheremany layers of a hierarchy are successfully trained in a robust manner. CNNs areknown for their robustness toward low variation in inputs; they require low prepro-cessing for their execution. They are also able to extract appropriate features whilesimultaneously performing discrimination [2, 4]. More specifically, in the currentimplementation, we make use of the LeNet [9] architecture for the convolutionalneural network.Our CNN has a total of eight layers comprising one initial input layer and fivehidden layers followed by one fully connected layer and ending with the outputclassifier layer. The convolution with the pooling layers acts as self-taught featureextractors from the input image (spatial representation of signal intensity), whilefully connected layer acts as a classifier. The essential purpose of convolutionis to extract features automatically from the input image. The dimensionality ofthese features is then reduced by the pooling layers. At the end of the model, thefully connected layer with a softmax activation function uses the learned high-levelfeatures to classify the input image into predefined classes (seizure and not seizure).In summary, LeNet model is composed of two main parts: the first part is the self-taught feature extraction model and the second part is the classification model. Inthe rest of this section, we will detail these two components.3.2.1 Feature Extraction ModelThe feature extraction model is the part where the network learns to detect differenthigh-level features from the input images. It consists of a sequence of convolutionand pooling layers.– Convolution map. The convolution layer is an elementary unit in a CNNarchitecture. The goal of convolution is to extract features from the input image.It consists of a set of learnable filters. Each filter is applied to the raw pixelEpileptic Seizure Detection Using a Convolutional Neural Network 83values of the image taking into account the red, green, and blue color channels ina sliding window fashion, computing the dot product between the filter pixel andthe input pixel. This will result in a two-dimensional activation map of the filtercalled feature map. Hence, the network learns filters that will activate when theyfind known features in the input. The CNN learns the values of these filters on itsown during the training process. The convolution operation is presented in Eq. 1.A convolution layer is configured by the number of convolution maps it containsMi and the size of the filters which are often squared kx  ky . The feature mapMi is computed as follows:Mi = bi +∑kWik  Xk (1)where  is the convolution operator, Xk is the kth input channel, Wik is thesubkernel of that channel, and bi is a bias term. In other words, the convolutionoperation being performed for each feature map is the sum of the application ofk different 2D squared convolution features plus a bias term. Hence, the powerof CNN is noted in its ability to learn the weights and biases of different featuremaps which lead to task-specific powerful feature extractors. Moreover, rectifiednonlinear activation functions (ReLU) are performed after every convolution tointroduce nonlinearity to the CNN.– Max-pooling map. In the architecture of convolutional neural network, convolu-tion layers are followed by subsampling layers. A layer of subsampling reducesthe size of the convolution maps and introduces invariance to (low) translationsthat can appear in the input. A layer of max-pooling is a variant of such layer thathas shown different benefits in its use. The output of max-pooling layer is givenby the maximum activation value in the input layer over subwindows within eachfeature map. The max-pooling operation reduces the size of the feature map.3.2.2 Classification ModelWithin the classification step, we use fully connected layers where each neuronprovides a full connection to all learned feature maps issued from the previous layerin the convolution neural network. These connected layers are based on the softmaxactivation function in order to compute the classes scores. The input of the softmaxclassifier is a vector of features resulting from the learning process, and the output isa probability that an image belongs to a given class. The softmax function ς takes asinput a C-dimensional vector z and outputs a C-dimensional vector y of real valuesbetween 0 and 1. This function is calculated as below:yc = ς(z)c = ezc∑Cd=1 ezdf or c = 1 · · ·C (2)In the next section, we will present the conducted experiments and results.84 B. Bouaziz et al.4 Experimental EvaluationTo validate the performance of the proposed approach, we conducted a set ofexperiments using real EEG dataset extracted from the Children’s Hospital Bostondatabase. This dataset consists of 36 bipolar 256 Hz EEG recordings from pediatricsubjects suffering from intractable seizures [12]. From these data, 300 images issuedfrom the transformation of EEG signal into intensity images have been generated.The images in our dataset are annotated as belonging to two different categorieswhich are normal or epileptic. Figure 3 illustrates some samples of the dataset whichcontains 300 images. Our EEG dataset images are obtained by generating intensityimages from EEG signals that belong to two groups: (a) healthy subjects and (b)epileptic subjects. The results of the full classification show that CNN has a potentialin the classification of EEG signals and detection of epileptic seizures by reaching99.48% of overall classification accuracy.As seen in Table 1, a stochastic gradient descent (SGD) algorithm is used in ourmodel to learn the best set of weights and biases of the neural network that minimizethe loss function. When learning, SGD works by randomly choosing a small numberof training inputs; we refer to this as the batch size which is set to 10. The learningrate set to 0.001 in our model is the rate at which a function moves through thesearch space. A small learning rate leads to more precise results but will cause moretraining time. The momentum considered as an additional factor to determine howfast SGD converges on the optimum point is set to 0.9.Furthermore, the effectiveness of our system will be evaluated by using acombination of precision, recall, and F1-measure. Table 2 shows this measuresacross all our experimental configurations.Fig. 3 EEG images dataset samplesTable 1 HyperparameterschoicesParameter ValueOptimization algorithm SGDLearning rate 0.01Momentum 0.9Weight decay 0.005Batch size 10Activation function SigmoidIterations 30Epileptic Seizure Detection Using a Convolutional Neural Network 85Table 2 Precision, recall, accuracy, and F1 score for the corresponding experimental configura-tionAccuracy Precision Recall F1 score0,9949 0,9949 0,9949 0,9949As mentioned before, these results were obtained using our deep learning modelfor the different two labels. In particular, we achieve a high overall classificationaccuracy when using a 20% of data for training and the rest for testing. As we cansee from Table 2, our model was able to find good results when applied to classifythe epileptic cases.5 ConclusionThis paper presented a new method for epileptic seizure detection based on EEGsignals. The method jointly uses the signals of all the electrodes in a spatialrepresentation. A CNN-based classifier is then used based on a manually annotateddatabase. The obtained results are promising with high accurate classification rates.Future work will focus on the combination of spatial and spectral representationsof the EEG signals to improve classification, in addition to the application of theproposed method to larger datasets.References1. Acharya U, Oh SL, Hagiwara Y, Tan J, Adeli H (2017) Deep convolutional neural network forthe automated detection and diagnosis of seizure using EEG signals. Comput Biol Med 17:1–92. Arel I, Rose, Derek C, Karnowski TP (2010) Deep machine learning – a new frontier inartificial intelligence research [Research Frontier]. IEEE Comput Int Mag 5(4):13–183. Bengio Y (2009) Learning deep architectures for AI. Foundations and trends in machinelearning, vol 2(1). Now Publishers, Hanover, pp 1–1274. Deng L (2014) A tutorial survey of architectures, algorithms, and applications for deeplearning. APSIPA Trans Signal Inf Process 3:1–295. Direito B, Teixeira C, Ribeiro B, Castelo-Branco M, Sales F, Dourado A (2012) Modelingepileptic brain states using EEG spectral analysis and topographicmapping. J NeurosciMethods 210(2):220–2296. Gotman J (2011) A few thoughts on – what is a seizure? Epilepsy Behav 22:S2–S37. Hosseini M, Pompili D, Elisevich K, Soltanian-Zadeh H (2018) Random ensemble learningfor EEG classification. Artif Intell Med 84:146–158. https://doi.org/10.1016/j.artmed.2017.12.004. Epub 3 Jan 20188. Kumar TS, Kanhanga V, Pachori RB (2015) Classification of seizure and seizure free EEGsignals using local binary patterns. Biomed Signal Process Control 15:33–409. LeCun Y, Boser B, Denker JS, Henderson D, Howard RE, Hubbard W, Jackel LD (1989)Backpropagation appliedto handwritten zip code recognition. Neural Comput 1(4):541–55186 B. Bouaziz et al.10. Liang SF, Wang HC, Chang WL (2010) Combination of EEG complexity and spectral analysisfor epilepsy diagnosis and seizure detection. EURASIP J Adv Signal Process 62:1–1511. Litjens GJS, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, van der Laak JAWM,van Ginneken B, Sánchez CI (2017) A Survey on Deep Learning in Medical Image Analysis.CoRR. http://arxiv.org/abs/1702.0574712. Shoeb A, Edwards H, Connolly J, Bourgeois B, Treves ST, Guttagf J (2004) Patient-specificseizure onset detection. Epilepsy Behav 5:483–49813. Yaffe RB et al (2015) Physiology of functional and effective networks in epilepsy. ClinNeurophysiol 126(2):227–23614. Zandi AS, Javidan M, Dumont GA, Tafreshi R (2010) Automated real-time epileptic seizuredetection in scalp EEG recordings using an algorithm based on wavelet packet transform. IEEETrans Biomed Eng 57(7):1639–165115. Zhang Z, Xu C, Feng W (2016) Road vehicle detection and classification based on deep neuralnetwork. In: IEEE International Conference on Software Engineering and Service Science(ICSESS), Beijing, 26–28 Aug 2016

Epileptic Seizure Detection Using a Convolutional Neural Network

The availability of electroencephalogram (EEG) data has opened up the possibility for new interesting applications, such as epileptic seizure detection. The detection of epileptic activity is usually performed by an expert based on the analysis of the EEG data. This paper shows how a convolutional neural network (CNN) can be applied to EEG images for a full and accurate classification. The proposed methodology was applied on images reflecting the amplitude of the EEG data over all electrodes. Two groups are considered: (a) healthy subjects and (b) epileptic subjects. Classification results show that CNN has a potential in the classification of EEG signals, as well as the detection of epileptic seizures by reaching 99.48% of overall classification accuracy.</p

Quintero-Rincón, Antonio

Heriot Watt Pure

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Epileptic Seizure Detection Using a Convolutional Neural Network

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