Synchrony Detection of Epileptic EEG Signals Based on Attention and Pearson’s Correlation Coefficient

Abstract

Epilepsy is generally considered as a collection of neurological disorders. Electroencephalography (EEG), a useful measure for analyzing the brain's electrical activity, has been widely used for the diagnosis of epileptic seizures. In an EEG diagnosis report, synchrony of epileptiform discharges should be included in the description. Correlation coefficient analysis could be used to measure the synchrony feature. However, the existing correlation coefficient overlooks the importance of epileptiform discharges in EEG data of epilepsy. In this paper, in order to tackle this problem, we propose a novel correlation coefficient to measure the synchrony feature. This method combines the attention mechanism with the correlation coefficient. We use the first-order difference to assign the attention weights and apply the weight to the Pearson's correlation coefficient. The first-order difference can highlight the high-frequency and high-amplitude waveforms in the original time series. Therefore, epileptiform discharges could play a more important role in the calculation of the correlation coefficient. We collected the EEG of epileptic patients during the interictal period and labeled the epileptiform discharge segments for experimental tests. In our case study, the Pearson's correlation coefficient with attention weights gave better results than the direct use of Pearson's correlation coefficient.