Integrating Bioimpedance Parameters For Enhanced Diagnosis And Severity Stratification Of Sleep Apnea

Abstract

Sleep apnea is a prevalent and underdiagnosed health condition characterized by repetitive episodes of airflow obstruction during sleep, leading to significant cardiovascular and metabolic risks. Despite advancements in diagnostic technologies, polysomnography remains the gold standard, though it is resource-intensive and not easily accessible. Bioimpedance, a non-invasive and cost-effective diagnostic tool, offers promise in overcoming these limitations by providing real-time physiological data correlated with respiratory function. This study aims to integrate bioimpedance parameters to enhance the diagnosis and severity stratification of sleep apnea. By analyzing bioimpedance metrics alongside traditional diagnostic measures, we demonstrate its potential to improve sensitivity, specificity, and practicality in clinical and home-based settings. The findings emphasize the feasibility of bioimpedance as a transformative approach in sleep medicine, fostering personalized management and early intervention for affected individuals.