Deep Learning for Epileptic Spike Detection - Le, Thanh Xuyen (H. : ĐHQGHN, 2018)
In the clinical diagnosis of epilepsy using electroencephalogram (EEG) data, an accurate automatic epileptic spikes detection system is highly useful and meaningful in that the conventional manual process is not only very tedious and time-consuming, but also subjective since it depends on the knowledge and experience of the doctors. In this paper, motivated by significant advantages and lots of achieved successes of deep learning in data mining, we apply Deep Belief Network (DBN), which is one of the breakthrough models laid the foundation for deep learning, to detect epileptic spikes in EEG data. It is really useful in practice because the promising quality evaluation of the spike detection system is higher than 90%. In particular, to construct the accurate detection model for non-spikes and spikes, a new set of detailed features of epileptic spikes is proposed that gives a good description of spikes. These features were then fed to the DBN which is modified from a generative model into a discriminative model to aim at classification accuracy. A performance comparison between using the DBN and other learning models including DAE, ANN, kNN and SVM was provided via numerical study by simulation. Accordingly, the sensitivity and specificity obtained by using the kind of deep learning model are higher than others. The experiment results indicate that it is possible to use deep learning models for epileptic spike detection with very high performance.