Abstract
We identify obstructive sleep apnea as the most common respiratory issue associated with sleep. Frequent breathing disruptions characterize sleep apnea during sleep due to an obstruction in the upper airway. This illness, if left untreated, can lead to significant health problems. This article outlines a sound approach for detecting sleep apnea and tracking it in an automated and intelligent manner. The method entails an automated identification of OSA based on the sound signal during breathing and a cardio-respiratory signals analysis for more efficient results. The suggested approach is put to the test under a variety of scenarios to verify its efficacy and dependability. The benefits and drawbacks of the suggested algorithm are mentioned further down.
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