Rule-based Sleep-Apnea detection algorithm

Obstructive Sleep Apnea Syndrome (OSAS) is a common sleep disorder characterized by repeated episodes of breathing cessation during sleep. It affects the quality of life and can lead to severe health complications. Continuous monitoring of Heart Rate Variability (HRV) and oxygen saturation (SpO2) can provide valuable insights into the presence and severity of sleep apnea. The algorithm herein proposed aims to identify the presence of OSAS and then to highly accurately differentiate (Severe, Moderate or Low) its severity level. The algorithm was evaluated on an online dataset; at the end of the algorithm assessment, a correlation coefficient of 98.65% was reached.