Identification of Salient Brain Regions for Anxiety Disorders Using Nonlinear EEG Feature Analysis.

In this paper, we present a novel approach for identifying salient brain regions and interpreting the ability of nonlinear EEG features to discriminate between anxiety disorders and healthy controls. The proposed method involves the integration of advanced EEG preprocessing and artefact correction, nonlinear feature extraction using conditional permutation entropy, and interpretable machine learning to identify relevant electrodes. The extracted nonlinear features show statistically significant differences between classes, demonstrating high discriminative ability.

Comparative performance of the EuroQol EQ-5D-5L and the CDC healthy days measures in assessing population health.

Objectives: To examine the comparative performance of EuroQol EQ-5D-5L and Center for Disease Control Healthy Days measures in assessing population health.

Methods: Using data from 2014 Alberta Community Health Survey, a cross-sectional population-based survey (N = 7559), conducted in Alberta, Canada, we examined construct validity of the measures as indicators of population health. Differences in EQ-5D-5L index score, visual analogue scale (EQ-VAS), and CDC unhealthy days index across socio-demographic subgroups were tested by Mann-Whitney and Kruskal-Wallis tests using known-groups approach.

A Multilayer LSTM Auto-Encoder for Fetal ECG Anomaly Detection.

The paper introduces a multilayer long short-term memory (LSTM) based auto-encoder network to spot abnormalities in fetal ECG. The LSTM network was used to detect patterns in the time series, reconstruct errors and classify a given segment as an anomaly or not. The proposed anomaly detection method provides a filtering procedure able to reproduce ECG variability based on the semi-supervised paradigm.