A Deep Learning Approach for Prediction of Epileptic Seizures Using EEG Signals

Abstract

Epilepsy is a brain disorder in which a patient undergoes frequent seizures. Around 30% of patients affected with epilepsy cannot be treated with medicines/surgical procedures. However, abnormal activity, known as the preictal state, starts some time before the seizure actually occurs. Therefore, it may be possible to deliver medication prior to the occurrence of a seizure if initiation of the preictal state is predicted before the seizure onset and it can also help in controlling the subsequent seizures. Electroencephalogram (EEG) signals are used to analyze the states of epileptic seizures which can be recorded by placing electrodes on scalp of subject known as scalp EEG signals or by implanting electrodes inside the brain on the surface called intracranial EEG signals. In this research, an epileptic seizure prediction method is proposed that predicts the start of preictal state before the seizure’s onset using scalp and intracranial EEG. Proposed epileptic seizure prediction method involves three steps; (i) Preprocessing of EEG signals, (ii) Features extraction and (iii) Classification of preictal and interictal states. In this method, EEG signals are preprocessed using empirical mode decomposition followed by bandpass filtering and conversion of time domain signals into frequency domain using short time Fourier transform. Class imbalance problem is mitigated by generating synthetic preictal segments using generative adversarial networks. A three layer customized convolutional neural network is proposed to extract automated features and combined with handcrafted features to get a comprehensive feature set. To reduce the effect of curse of dimensionality, correlated features have been dropped from feature set using Pearson correlation coefficient and an optimal subset of features has been selected using particle swarm optimization. Feature set is then used to train an ensemble classifier that combines Support Vector Machine (SVM), Convolutional Neural Network (CNN) and Long Short Term Memory Units (LSTMs) using Model agnostic meta learning. CHBMIT scalp EEG and American epilepsy society-Kaggle seizure prediction challenge intracranial EEG datasets have been used to train and test the proposed method. An average sensitivity of 96.28 %ii and specificity of 95.65 % with average anticipation time of 33 minutes on all subjects of CHBMIT has been achieved by proposed method. On American epilepsy society-Kaggle seizure prediction dataset, an average sensitivity and specificity of 94.2 % and 95.8 % has been achieved on all subjects. Results achieved by proposed method have been compared with the existing state of the art epileptic seizure prediction methods. Proposed method is able to achieve more than 3 % sensitivity, specificity and average anticipation time compared to existing methods.