Automated detection of sleep apnea and hypopnea events based on robust airflow envelope tracking

The paper presents a new approach to detection of apnea/hypopnea events, in the presence of artifacts and breathing irregularities, from a single-channel airflow record. The proposed algorithm identifies segments of signal affected by a high amplitude modulation corresponding to apnea/hypopnea events. It is shown that a robust airflow envelope-free of breathing artifacts-improves effectiveness of the diagnostic process and allows one to localize the beginning and the end of each episode more accurately. The performance of the approach, evaluated on 30 overnight polysomnographic recordings, was assessed using diagnostic measures such as accuracy, sensitivity, specificity, and Cohen's coefficient of agreement; achieving 95%, 90%, 96%, and 0.82, respectively.