Automatic detection of sleep apnea from a single-lead ECG signal based on spiking neural network model.

Background: Sleep apnea (SLA) is a commonly encountered sleep disorder characterized by repetitive cessation of respiration while sleeping. In the past few years, researchers have focused on developing less complex and more cost-effective diagnostic approaches for identifying SLA recipients, in contrast to the cumbersome, complicated, and expensive conventional methods.

Method: This study presents a biologically plausible learning approach of spiking neural networks (SNN) with temporal coding and a tempotron learning model for diagnosing SLA disorder using single-lead electrocardiogram (ECG) data information.

Systematic review of automated sleep apnea detection based on physiological signal data using deep learning algorithm: a meta-analysis approach.

Unlabelled: Sleep apnea (SLA) is a respiratory-related sleep disorder that affects a major proportion of the population. The gold standard in sleep testing, polysomnography, is costly, inconvenient, and unpleasant, and it requires a skilled professional to score. Multiple researchers have suggested and developed automated scoring processes with less detectors and automated classification algorithms to resolve these problems.

Effect of feeding rice based distillers dried grains with solubles and gluten meal on nutrient transporter genes and immunity in broiler chickens.

Aim: The aim of this study was to investigate the effect of feeding rice based distillers dried grains with solubles (rDDGS) and gluten meal on nutrient transporter genes and immunity in broiler chickens.

Materials And Methods: A 2×3 factorial design resulted in six experimental diets, namely T1 (no rDDGS/rice gluten meal [RGM]/enzyme), T2 (no rDDGS/RGM, with multienzymes), T3 (12.5% rDDGS, 15% RGM, no enzyme), T4 (12.