Recurrent Neural Networks and Their Application in Seizure Classification

Abstract

Deep learning (DL) architectures such as deep neural networks (DNN), deep belief networks (DBN), recurrent neural networks(RNN) and convolutional neural networks (CNN) have been applied to applications such as computer vision, speech recognition, natural language processing, audio recognition, social network filtering, machine translation, bioin-formatics, drug design, medical image analysis, material inspection and board game programs, in which has comparable performance than human experts. With the growing interest and research in the area of artificial neural network, deep neural network enable computers to get trained for error-free diagnosis to diseases like epilepsy. In literature, researchers carried out many mathematical models for pre-processing of EEG data and classification between seizure and seizure free signals or different 166types of network disorders. The introduction of various algorithms like machine learning deep learning, etc., in artificial intelligence, aids to classify the data with or without pre-processing and two class system. It is important to try multi-class time series classification of various brain activities (tumour, network disorders) using the sophisticated algorithms. In this chapter, different deep learning algorithms for multiclass, time series classification of different electrical activities in brain are discussed. The main focus is on the application of different RNN models in seizure classification of Electroencephalogram (EEG) signals. It is very important to interpret the 1D EEG signals and classify among different activities of brain for various diagnostic purpose. The fully interconnected hidden configuration of recurrent neural network (RNN) makes the model very dominant which enable to discover temporal correlations between far away events in the data. The training of RNN architecture when used in deep network is challenging because of vanishing/exploding gradient in deeper layer. This paper aims to perform multiclass time series classification of EEG signal using three different RNN techniques; simple Recurrent Neural Network, Long-Short Term Memory (LSTM) and GRUs. A comparative study between RNNs is done in terms of configuration, time taken and accuracy for EEG signals acquired from people having different pathological and physiological brain states. The accuracy and time taken for multilayer recurrent neural networks are determined for classification of EEG for five different classes using three different types of RNN networks, for 1 to 1024 units with 100 epochs and 5 different layers of 32 cells with 300 epochs, with a learning rate of 0.01. It has been observed that the number of layers increases the time complexity and provides constant accuracy for more than three layers. Further, it can be extended for the accuracy and time consumption for different batch sizes with different epochs to fix a proper network without over fitting the network.