Validation of a Sleep Apnea Screening System Using Only Tracheal Sounds

Sleep-related Breathing Disorders (SRBD) are one of the most common sleep disorders. Patients suffer from repetitions of complete stops (apneas) or partial reductions (hypopneas) in respiratory flow during sleep. The standard method for the diagnosis of sleep apnea is the attended cardiorespiratory polysomnography (PSG). However, this method is expensive, and the extensive recording equipment can have a significant impact on sleep quality, falsifying the results. This work aims to investigate and validate a simplified automated system that uses only a tracheal sound (TS) sensor. A signal processing-based algorithm was implemented to detect respiratory events and thus estimate the Apnea Hypopnea Index (AHI). For validation, 13 full-night PSG recordings were analyzed. The present method consists of using only TS for respiratory event detection. Considering PSG as a gold standard reference, the developed approach reached the following results: For apnea detection, positive predictive value (PPV) reached 82%, and the sensitivity was 90%. For hypopnea detection, PPV reached 40%, and the sensitivity was 58%. For AHI estimation, 93%accuracy was achieved. In conclusion, we worked on a new, simpler approach than PSG, yet very promising, to reliably detect apnea and estimate the AHI score. However, hypopnoea detection still needs more future improvements.