Detecting emotions through EEG signals based on modified convolutional fuzzy neural network.

Emotion is a human sense that can influence an individual's life quality in both positive and negative ways. The ability to distinguish different types of emotion can lead researchers to estimate the current situation of patients or the probability of future disease. Recognizing emotions from images have problems concealing their feeling by modifying their facial expressions.

Focality in childhood absence epilepsy.

Background And Purpose: Childhood absence epilepsy (CAE) has a typical electroencephalography (EEG) pattern of generalized 3 Hz spike and wave discharges (SWD). Focal interictal discharges were also documented in a small number of documents. The aim was to investigate the amplitudes of interictal 3 Hz SWD within the 1 second in drug-naïve CAE patients.

Prediction of osteoporosis using MRI and CT scans with unimodal and multimodal deep-learning models.

Purpose: Osteoporosis is the systematic degeneration of the human skeleton, with consequences ranging from a reduced quality of life to mortality. Therefore, the prediction of osteoporosis reduces risks and supports patients in taking precautions. Deep-learning and specific models achieve highly accurate results using different imaging modalities.

COVID-19 Lung CT image segmentation using localization and enhancement methods with U-Net.

Segmentation of pneumonia lesions from Lung CT images has become vital for diagnosing the disease and evaluating the severity of the patients during the COVID-19 pandemic. Several AI-based systems have been proposed for this task. However, some low-contrast abnormal zones in CT images make the task challenging.

A Near-Global Slowing of Background Activity and Epileptic Discharges in Children With Mild to Moderately Symptomatic COVID-19 Infection: An Electro-Neurophysiological Study.

. To assess the functional involvement of the central nervous system (CNS) via quantitative electroencephalography (EEG) analysis in children with mild to moderate COVID-19 infection who were otherwise previously healthy children. .