Automated Detection of Sleep Apnea and Hypopnea Events Based on Robust Airflow Envelope Tracking in the Presence of Breathing Artifacts

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, based on a robust envelope detector, 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 proposed approach, evaluated on 30 overnight polysomnographic recordings, was assessed using diagnostic measures such as accuracy, sensitivity, specificity, and Cohen's coefficient of agreement; the achieved levels were equal to 95%, 90%, 96%, and 0.82, respectively. The results suggest that the algorithm may be implemented successfully in portable monitoring devices, as well as in software packages used in sleep laboratories for automated evaluation of sleep apnea/hypopnea syndrome.