Automated sleep breath disorders detection utilizing patient sound analysis

Abstract Results of clinical studies suggest that there is a relationship between breathing-related sleep disorders and behavioral disorder and health effects. Apnea is considered one of the major sleep disorders with great accession in population and significant impact on patient's health. Symptoms include disruption of oxygenation, snoring, choking sensations, apneic episodes, poor concentration, memory loss, and daytime somnolence. Diagnosis of apnea and breath disorders involves monitoring patient's biosignals and breath during sleep in specialized clinics requiring expensive equipment and technical personnel. This paper discusses the design and technical details of an integrated low-cost system capable for preliminary detection of sleep breath disorders at patient's home utilizing patient sound signals. The paper describes the proposed architecture and the corresponding HW and SW modules, along with a preliminary evaluation.

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