Cost-Sensitive Deep Active Learning for Epileptic Seizure Detection

Abstract

The analysis of electroencephalogram (EEG) signal plays a crucial role in epileptic seizure detection. Researchers have proposed many machine learning and deep learning based automatic epileptic seizure detection methods. However, these schemes, especially the deep learning based ones, suffer from labeling huge amounts of training data. Moreover, in epileptic seizure detection, physicians pay more attention to abnormal signals than normal signals, and thus the misclassification cost for them should be different. To address these issues, we propose a cost-sensitive deep active learning scheme to detect the epileptic seizure. In particular, we develop a new generic double-deep neural network (double-DNN) to obtain the cost-sensitive utility for the samples selection strategy in the labeling process. We further employ three types of fundamental neural networks, i.e., one-dimensional convolutional neural networks (1D CNNs), recurrent neural networks with long short-term memory (LSTM) units, and recurrent neural networks with gated recurrent units (GRU), in the double-DNN and evaluate their performances. Experiment results show that the proposed scheme can reduce the amount of labeled samples by up to 33% and 80% compared with uncertainty sampling and random sampling, respectively.