DocumentCode :
2381467
Title :
Automatic differentiation of obstructive and central hypopneas with esophageal pressure measurement during sleep
Author :
Morgenstern, C. ; Schwaibold, M. ; Randerath, W. ; Bolz, A. ; Jané, R.
Author_Institution :
Dept. ESAII, Univ. Politec. de Catalunya, Barcelona, Spain
fYear :
2009
fDate :
3-6 Sept. 2009
Firstpage :
7102
Lastpage :
7105
Abstract :
The differentiation between obstructive and central respiratory events is one of the most recurrent tasks in the diagnosis of sleep disordered breathing. Esophageal pressure measurement is the gold-standard method to assess respiratory effort and identify these events. But as its invasiveness discourages its use in clinical routine, non-invasive systems have been proposed for differentiation. However, their adoption has been slow due to their limited clinical validation, as the creation of manual, gold-standard validation sets by human experts is a cumbersome procedure. In this study, a new system is proposed for an objective automatic, gold-standard differentiation between obstructive and central hypopneas with the esophageal pressure signal. First, an overall of 356 hypopneas of 16 patients were manually scored by a human expert to create a gold-standard validation set. Then, features were extracted from each hypopnea to train and test classifiers (discriminant analysis, support vector machines and adaboost classifiers) to differentiate between central and obstructive hypopneas with the gold-standard esophageal pressure signal. The automatic differentiation system achieved promising results, with a sensitivity of 0.88, a specificity of 0.93 and an accuracy of 0.90. Hence, this system seems promising for an automatic, gold-standard differentiation between obstructive and central hypopneas.
Keywords :
biomedical measurement; feature extraction; medical signal processing; pneumodynamics; signal classification; sleep; support vector machines; adaboost classifiers; automatic hypopnea differentiation; central hypopnea; discriminant analysis; esophageal pressure measurement; feature extraction; obstructive hypopnea; respiratory events; sleep; sleep disordered breathing; support vector machines; Adult; Aged; Algorithms; Artificial Intelligence; Biomedical Engineering; Diagnosis, Computer-Assisted; Diagnosis, Differential; Esophagus; Humans; Male; Middle Aged; Polysomnography; Pressure; Sleep Apnea, Central; Sleep Apnea, Obstructive; Young Adult;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Engineering in Medicine and Biology Society, 2009. EMBC 2009. Annual International Conference of the IEEE
Conference_Location :
Minneapolis, MN
ISSN :
1557-170X
Print_ISBN :
978-1-4244-3296-7
Electronic_ISBN :
1557-170X
Type :
conf
DOI :
10.1109/IEMBS.2009.5332900
Filename :
5332900
Link To Document :
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