title: An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas.
abstract: The automatic 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. This study presents a new classifier that automatically differentiates obstructive and central hypopneas with the Pes signal and a new approach for an automatic noninvasive classifier with nasal airflow. An overall of 28 patients underwent night polysomnography with Pes recording, and a total of 769 hypopneas were manually scored by human experts to create a gold-standard annotation set. Features were automatically extracted from the Pes signal to train and test the classifiers (discriminant analysis, support vector machines, and adaboost). After a significantly (p < 0.01) higher incidence of inspiratory flow limitation episodes in obstructive hypopneas was objectively, invasively assessed compared to central hypopneas, the feasibility of an automatic noninvasive classifier with features extracted from the airflow signal was demonstrated. The automatic invasive classifier achieved a mean sensitivity, specificity, and accuracy of 0.90 after a 100-fold cross validation. The automatic noninvasive feasibility study obtained similar hypopnea differentiation results as a manual noninvasive classification algorithm. Hence, both systems seem promising for the automatic differentiation of obstructive and central hypopneas.
authors.affiliation: Institut de Bioenginyeria de Catalunya (IBEC), Departament d'Enginyeria de Sistemes, Automàtica i Informàtica Industrial, Universitat Politècnica de Catalunya, and Centro de Investigación Biomédica en Red de Bioingeniería, Biomateriales y Nanomedicina, E-08028 Barcelona, Spain. cmorgenstern@ibec.pcb.ub.es
authors.forename: Christian
authors.initials: C
authors.lastname: Morgenstern
authors.forename: Matthias
authors.initials: M
authors.lastname: Schwaibold
authors.forename: Winfried J
authors.initials: WJ
authors.lastname: Randerath
authors.forename: Armin
authors.initials: A
authors.lastname: Bolz
authors.forename: Raimon
authors.initials: R
authors.lastname: Jané
mesh_terms: D000328:Adult; D000368:Aged; D000465:Algorithms; D003937:Diagnosis, Differential; D016002:Discriminant Analysis; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated; D017286:Polysomnography / Q000379:methods*; D015203:Reproducibility of Results; D012815:Signal Processing, Computer-Assisted; D020182:Sleep Apnea, Central / Q000175:diagnosis* / Q000503:physiopathology; D020181:Sleep Apnea, Obstructive / Q000175:diagnosis* / Q000503:physiopathology