EEG correlates of quality of life and associations with seizure without awareness and depression in patients with epilepsy.

Abstract

AimsQuality of life (QOL) is an important issue for not only patients with epilepsy but also physicians. Depression has a large impact on QOL. Nonlinear electroencephalogram (EEG) analysis using machine learning (ML) has the potential to improve the accuracy of the diagnosis of epilepsy. Therefore, in this study, we examined EEG nonlinearity, EEG correlates of QOL in patients with epilepsy, and the accuracy of EEG for the interval from seizure without awareness (SA–) and for depression, using ML.MethodsThe Side Effects and Life Satisfaction (SEALS) inventory was used to assess QOL, and the Neurological Disorders Depression Inventory for Epilepsy (NDDI‐E) was used as a screening tool for depression on the date of the EEG recording. EEG with wavelet denoising (WD), the Savitzky–Golay filter, and non‐denoising were created in combination with low‐ and high‐pass filters. These EEG sets were adopted for phase space reconstruction methods. Using a generalized linear mixed‐effects model for SEALS, sample entropy as a measurement of regularity, SA–, seizure with awareness, and depression were examined.ResultsWD and non‐denoising EEG sets in the bilateral posterior temporal‐occipital, centro‐parietal, parieto‐occipital, and Fz–Cz of the 10–20 method were associated with SEALS and demonstrated nonlinearity, and the moderate effects of classification for the interval elapsed from SA– and for depression. When the intervals from SA– were added, the effects of the EEG classification for depression increased.ConclusionThese findings suggest that EEG regions associated with QOL showing nonlinearity are useful for classifying SA– and depression.