“Bioimpedance Analysis for Assessing Association Of Sleep Apnea Severity And BioimpedenceParameters -A Groundbreaking Study”

Sleep apnea, a prevalent yet underdiagnosed disorder, poses significant risks to cardiovascular and metabolic health. Traditional diagnostic methods, such as polysomnography, are often time-consuming, expensive, and inaccessible for widespread use. This study explores the application of advanced bioimpedance analysis (BIA) as a non-invasive, cost-effective tool for accurately classifying the severity of sleep apnea. By leveraging machine learning algorithms, critical features were extracted from bioimpedance signals to classify apnea severity levels efficiently. The proposed method demonstrated high accuracy, sensitivity, and specificity in distinguishing mild, moderate, and severe cases of sleep apnea. The findings suggest that BIA, combined with robust computational models, offers a promising alternative for scalable and reliable sleep apnea diagnostics.