Epileptic Spike Detection by Using a Linear-Phase Convolutional Neural Network

To cope with the lack of highly skilled professionals, machine leaning with proper signal techniques is a key to establishing automated diagnostic-aid technologies to conduct epileptic electroencephalogram (EEG) testing. In particular, frequency filtering with appropriate passbands is essential to enhance biomarkers—such as epileptic spike waves—that are noted in the EEG. This paper introduces a novel class of convolutional neural networks (CNNs) having a bank of linear-phase finite impulse response filters at the first layer. These may behave as bandpass filters that extract biomarkers without destroying waveforms because of linear-phase condition. The proposed CNNs were trained with a large amount of clinical EEG data, including 15,899 epileptic spike waveforms recorded from 50 patients. These have been labeled by specialists. Experimental results show that the trained data-driven filter bank with supervised learning is dyadic like discrete wavelet transform. Moreover, the area under the curve achieved above 0.9 in most cases.

Human and Rodent Temporal Lobe Epilepsy is Characterized by Changes in O-GLCNAC Homeostasis that can be Reversed to Dampen Epileptiform Activity

Temporal Lobe Epilepsy (TLE) is frequently associated with changes in protein composition and post-translational modifications (PTM) that exacerbate the disorder. O-linked-β-N-acetyl glucosamine (O-GlcNAc) is a PTM occurring at serine/threonine residues that integrate energy supply with demand. The enzymes O-GlcNActransferase (OGT) and O-GlcNAcase (OGA) mediate the addition and removal, respectively, of the O-GlcNAc modification. The goal of this study was to determine whether changes in OGT/OGA cycling and disruptions in protein O-GlcNAcylation occur in the epileptic hippocampus. We observed reduced global and protein specific O-GlcNAcylation and OGT expression in the kainate rat model of TLE and in human TLE hippocampal tissue. Inhibiting OGA with Thiamet-G elevated protein O-GlcNAcylation, and decreased both seizure duration and epileptic spike events, suggesting that OGA may be a therapeutic target for seizure control. These findings suggest that loss of O-GlcNAc homeostasis in the kainate model and in human TLE can be reversed via targeting of O-GlcNAc related pathways.

Deep Learning for Epileptic Spike Detection

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 accurate detection model for non-spikes and spikes, a new set of detailed features of epileptic spikes is proposed. These features were then fed to the DBN which is modified from a generative model into a discriminative model to aim at classification accuracy. The experiment results indicate that it is possible to use deep learning models for epileptic spike detection with very high performance in item of sensitivity, selectivity, specificity and accuracy 92.82%, 97.83% , 96.41%, and 96.87%, respectively.