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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
Convolutional Neural Network
Bassem Bouaziz, Lotfi Chaari, Hadj Batatia, and Antonio Quintero-Rincón
Abstract 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.
Keywords Epilepsy · CNN · Seizure detection · EEG
B. Bouaziz ()
MIRACL Laboratory, University of Sfax, Sfax, Tunisia e-mail:Bassem.Bouaziz@isims.usf.tn
L. Chaari
MIRACL Laboratory, University of Sfax, Sfax, Tunisia Digital Research Centre of Sfax, Sfax, Tunisia e-mail:Lotfi.Chaari@isims.usf.tn
H. Batatia
IRIT, University of Toulouse, Toulouse, France e-mail:hadj.batatia@inp-toulouse.fr
A. Quintero-Rincón
Department of Bioengineering, Instituto Tecnológico de Buenos Aires (ITBA), Buenos Aires, Argentina
e-mail:aquinter@itba.edu.ar
© Springer Nature Switzerland AG 2019
L. Chaari (ed.), Digital Health Approach for Predictive, Preventive, Personalised
and Participatory Medicine, Advances in Predictive, Preventive and Personalised
Medicine 10,https://doi.org/10.1007/978-3-030-11800-6_9
80 B. Bouaziz et al.
1
Introduction
Epilepsy, affecting more than 60 million people of the worldwide population, is a nervous system disorder characterized by seizures. These seizures are commonly defined as sudden disturbances in brain functions that indicate hyper-synchronous activity of neurons in the brain [6,13]. Hence, epilepsy disease often leads to loss of upright posture and impairment of the senses that can lead to serious injuries and sometimes 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 on time. As a clinical tool, the electroencephalogram (EEG) signal is very important in the diagnosis of epilepsy. It provides a snapshot of the human brain while performing a cognitive task or even at a resting state. The EEG is collected by placing electrodes on the scalp of the patient and can then record the electro-activity produced by the brain. Traditionally, diagnosis using EEG signals is performed by experts based on the visual inspection to detect seizures. Such a costly and slow process is subject to human errors. It can also lead to ambiguous interpretations and even disagreements between experts. Recent works achieved seizure detection by preprocessing data collected from electrodes. This processing is followed by a hand-crafted feature extraction step based on signal properties to be analyzed and classified 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 based on images generated from EEG signals. The algorithm takes an intensity image calculated from EEG data as an input and classifies the images into epileptic and healthy. The learning is performed on the intensity images gathering signals from all the used electrodes. Operating on 200 epileptic and 500 healthy images, experiments show that our system performs 98.48 % overall accuracy.
The remainder of this paper is organized as follows. An overview of existing techniques used for seizure detection from EEG data is presented in the second section. The third section is dedicated to the proposed approach. Section4describes the experimental validation of the proposed method. The last section is dedicated to the conclusion and to draw the future direction of this work.
2
Related Work
For seizure detection, several approaches have been developed in the recent literature 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 for most of the developed methods. Either authors decompose the EEG signal and use appropriate components, or they extract appropriate features from the signal to perform classification. In this sense, even if the features type is crucial for the accuracy of seizure detection, choosing an appropriate classification algorithm is important too.
In 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 data and powerful computing infrastructure made CNN a suitable candidate for the current application. This CNN has demonstrated its ability to provide high good classification rates in a number of applications such as vehicle detection [15] and medical imaging [11].
3
Proposed Approach
To deal with the mentioned challenges, we introduce a deep learning-based approach to classify and identify epileptic seizure. The general architecture of the proposed framework is illustrated in Fig.1. The figure shows that the pipeline consists of two main steps: dataset construction and convolutional neural network (CNN). The CNN contains self-taught feature extraction and classification components. In the following, we describe each step.
3.1
Dataset Construction
This process consists of constructing input data for the CNN model in order to detect seizure. Starting from continuous EEG signals issued from annotated dataset of 23 electrodes, we extract a fixed temporal window of 2 s. The obtained windows are then transformed to a spatial representation by producing a set of intensity images as shown in Fig.2b.
82 B. Bouaziz et al.
Fig. 2 Temporal EEG signals of the 23 electrodes (a) and the spatial representation (b)
3.2
CNN for Seizure Detection
Convolutional neural networks (CNNs) are a family of multilayer neural networks and are considered as the first successful trial for deep learning approach where many layers of a hierarchy are successfully trained in a robust manner. CNNs are known for their robustness toward low variation in inputs; they require low prepro-cessing for their execution. They are also able to extract appropriate features while simultaneously performing discrimination [2,4]. More specifically, in the current implementation, we make use of the LeNet [9] architecture for the convolutional neural network.
Our CNN has a total of eight layers comprising one initial input layer and five hidden layers followed by one fully connected layer and ending with the output classifier layer. The convolution with the pooling layers acts as self-taught feature extractors from the input image (spatial representation of signal intensity), while fully connected layer acts as a classifier. The essential purpose of convolution is to extract features automatically from the input image. The dimensionality of these features is then reduced by the pooling layers. At the end of the model, the fully connected layer with a softmax activation function uses the learned high-level features 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. In the rest of this section, we will detail these two components.
3.2.1 Feature Extraction Model
The feature extraction model is the part where the network learns to detect different high-level features from the input images. It consists of a sequence of convolution and pooling layers.
– Convolution map. The convolution layer is an elementary unit in a CNN architecture. 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 pixel
values of the image taking into account the red, green, and blue color channels in a sliding window fashion, computing the dot product between the filter pixel and the input pixel. This will result in a two-dimensional activation map of the filter called feature map. Hence, the network learns filters that will activate when they find known features in the input. The CNN learns the values of these filters on its own during the training process. The convolution operation is presented in Eq.1. A convolution layer is configured by the number of convolution maps it contains
Mi and the size of the filters which are often squared kx ky. The feature map
Mi is computed as follows:
Mi = bi +
k
Wik Xk (1)
where is the convolution operator, Xk is the kth input channel, Wik is the
subkernel of that channel, and bi is a bias term. In other words, the convolution
operation being performed for each feature map is the sum of the application of
k different 2D squared convolution features plus a bias term. Hence, the power
of CNN is noted in its ability to learn the weights and biases of different feature maps which lead to task-specific powerful feature extractors. Moreover, rectified nonlinear activation functions (ReLU) are performed after every convolution to introduce 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 reduces the size of the convolution maps and introduces invariance to (low) translations that can appear in the input. A layer of max-pooling is a variant of such layer that has shown different benefits in its use. The output of max-pooling layer is given by the maximum activation value in the input layer over subwindows within each feature map. The max-pooling operation reduces the size of the feature map.
3.2.2 Classification Model
Within the classification step, we use fully connected layers where each neuron provides a full connection to all learned feature maps issued from the previous layer in the convolution neural network. These connected layers are based on the softmax activation function in order to compute the classes scores. The input of the softmax classifier is a vector of features resulting from the learning process, and the output is a probability that an image belongs to a given class. The softmax function ς takes as input a C-dimensional vector z and outputs a C-dimensional vector y of real values between 0 and 1. This function is calculated as below:
yc= ς(z)c= e zc
C
d=1ezd
f or c = 1 · · · C (2)
84 B. Bouaziz et al.
4
Experimental Evaluation
To validate the performance of the proposed approach, we conducted a set of experiments using real EEG dataset extracted from the Children’s Hospital Boston database. This dataset consists of 36 bipolar 256 Hz EEG recordings from pediatric subjects suffering from intractable seizures [12]. From these data, 300 images issued from the transformation of EEG signal into intensity images have been generated. The images in our dataset are annotated as belonging to two different categories which are normal or epileptic. Figure3illustrates some samples of the dataset which contains 300 images. Our EEG dataset images are obtained by generating intensity images 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 potential in the classification of EEG signals and detection of epileptic seizures by reaching 99.48% of overall classification accuracy.
As seen in Table1, a stochastic gradient descent (SGD) algorithm is used in our model to learn the best set of weights and biases of the neural network that minimize the loss function. When learning, SGD works by randomly choosing a small number of training inputs; we refer to this as the batch size which is set to 10. The learning rate set to 0.001 in our model is the rate at which a function moves through the search space. A small learning rate leads to more precise results but will cause more training time. The momentum considered as an additional factor to determine how fast SGD converges on the optimum point is set to 0.9.
Furthermore, the effectiveness of our system will be evaluated by using a combination of precision, recall, and F1-measure. Table 2 shows this measures across all our experimental configurations.
Fig. 3 EEG images dataset samples Table 1 Hyperparameters
choices Parameter Value
Optimization algorithm SGD Learning rate 0.01
Momentum 0.9
Weight decay 0.005
Batch size 10
Activation function Sigmoid
Table 2 Precision, recall, accuracy, and F1 score for the corresponding experimental configura-tion
Accuracy Precision Recall F1 score
0,9949 0,9949 0,9949 0,9949
As mentioned before, these results were obtained using our deep learning model for the different two labels. In particular, we achieve a high overall classification accuracy when using a 20% of data for training and the rest for testing. As we can see from Table2, our model was able to find good results when applied to classify the epileptic cases.
5
Conclusion
This paper presented a new method for epileptic seizure detection based on EEG signals. The method jointly uses the signals of all the electrodes in a spatial representation. A CNN-based classifier is then used based on a manually annotated database. The obtained results are promising with high accurate classification rates. Future work will focus on the combination of spatial and spectral representations of the EEG signals to improve classification, in addition to the application of the proposed method to larger datasets.
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