Automatic non-invasive differentiation of obstructive and central hypopneas with nasal airflow compared to esophageal pressure
Tipus de documentText en actes de congrés
Condicions d'accésAccés obert
The differentiation of obstructive and central respiratory events is a major challenge in the diagnosis of sleep disordered breathing. Esophageal pressure (Pes) measurement is the gold-standard method to identify these events but its invasiveness deters its usage in clinical routine. Flattening patterns appear in the airflow signal during episodes of inspiratory flow limitation (IFL) and have been shown with invasive techniques to be useful to differentiate between central and obstructive hypopneas. In this study we present a new method for the automatic non-invasive differentiation of obstructive and central hypopneas solely with nasal airflow. An overall of 36 patients underwent full night polysomnography with systematic Pes recording and a total of 1069 hypopneas were manually scored by human experts to create a goldstandard annotation set. Features were automatically extracted from the nasal airflow signal to train and test our automatic classifier (Discriminant Analysis). Flattening patterns were non-invasively assessed in the airflow signal using spectral and time analysis. The automatic non-invasive classifier obtained a sensitivity of 0.71 and an accuracy of 0.69, similar to the results obtained with a manual non-invasive classification algorithm. Hence, flattening airflow patterns seem promising for the noninvasive differentiation of obstructive and central hypopneas.
CitacióMorgenstern, C.R. [et al.]. Automatic non-invasive differentiation of obstructive and central hypopneas with nasal airflow compared to esophageal pressure. A: Annual International Conference of the IEEE Engineering in Medicine and Biology Society. "2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society". Buenos Aires: 2010, p. 6142-6145.
Versió de l'editorhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5627787&tag=1