An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas
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Cita com:
hdl:2117/10135
Tipus de documentArticle
Data publicació2010-04-15
Condicions d'accésAccés obert
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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 classifierwith 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 fromthe 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.
CitacióMorgenstern, C.R. [et al.]. An invasive and a noninvasive approach for the automatic differentiation of obstructive and central hypopneas. "IEEE transactions on biomedical engineering", 15 Abril 2010, vol. 57, núm. 8, p. 1927-1936.
ISSN0018-9294
Versió de l'editorhttp://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=5447740&tag=1
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