Examining nonlinearity using complexity and entropy

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

Aims

Quality 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.

Methods

The 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.

Results

WD 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.

Conclusion

These findings suggest that EEG regions associated with QOL showing nonlinearity are useful for classifying SA– and depression.

Wavelet denoising 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 the Side Effects and Life Satisfaction inventory and demonstrated nonlinearity, and the moderate effects of classification for the interval elapsed from seizure without awareness and for depression. When the intervals from seizure without awareness were added, the effects of the EEG classification for depression increased.

Electroencephalography as a Non-Invasive Biomarker of Alzheimer's Disease: A Forgotten Candidate to Substitute CSF Molecules?

Biomarkers for disease diagnosis and prognosis are crucial in clinical practice. They should be objective and quantifiable and respond to specific therapeutic interventions. Optimal biomarkers should reflect the underlying process (pathological or not), be reproducible, widely available, and allow measurements repeatedly over time. Ideally, biomarkers should also be non-invasive and cost-effective. This review aims to focus on the usefulness and limitations of electroencephalography (EEG) in the search for Alzheimer's disease (AD) biomarkers. The main aim of this article is to review the evolution of the most used biomarkers in AD and the need for new peripheral and, ideally, non-invasive biomarkers. The characteristics of the EEG as a possible source for biomarkers will be revised, highlighting its advantages compared to the molecular markers available so far.