Automatic classification of inspiratory flow limitation assessed non-invasively during sleep

Proceedings of the 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS'08 - "Personalized Healthcare through Technology"

Volumn , Issue , 2008, Pages 1132-1135

  Morgenstern, C ^a   Jane R ^a   Schwaibold, M ^b   Randerath, W ^c  

^a BIOMATERIALES Y NANOMEDICINA CIBER BBN   (Spain)

^b DR WILLMAR SCHWABE GMBH AND CO KG   (Germany)

^c Krankenhaus Bethanien Solingen   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ADAPTIVE BOOSTING; MEDICINE; PRESSURE MEASUREMENT; SUPPORT VECTOR MACHINES;

ADA BOOST CLASSIFIERS; AUTOMATIC ANNOTATION; AUTOMATIC CLASSIFICATION; AUTOMATIC CLASSIFICATION SYSTEMS; CLASSIFICATION RESULTS; INSPIRATORY FLOW LIMITATIONS; RESPIRATORY EFFORTS; SLEEP-DISORDERED BREATHING;

SLEEP RESEARCH;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; HUMAN; INHALATION; METHODOLOGY; PATHOPHYSIOLOGY; POLYSOMNOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SLEEP DISORDERED BREATHING; SPIROMETRY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; DIAGNOSIS, COMPUTER-ASSISTED; HUMANS; INHALATION; PATTERN RECOGNITION, AUTOMATED; POLYSOMNOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SLEEP APNEA SYNDROMES; SPIROMETRY;

EID: 61849132727     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/iembs.2008.4649360     Document Type: Conference Paper

References (17)

1. Guilleminault C, Stoohs R, Clerk A, Cetel M, Maistros P. A cause of excessive daytime sleepiness. The upper airway resistance syndrome" Chest 104:781-787 1993
2. Hosselet J., Norman R., Ayappa A., Rapoport D., Detection of Flow Limitation with a Nasal Cannula/Pressure Transducer System, Am J Respir Crit Care Med Vol 157. pp 1461-1467, 1998
3. S. A. Clark, C. R. Wilson, M. Sato, D. Pegelow, J. A. Dempsey, Assessment of Inspiratory Flow Limitation Invasively and Noninvasively during Sleep, Am J Respir Crit Care Med, 158, pages 713-722, 1998
4. R. Tamisier, J. L. Pepin, B. Wuyam, R. Smith, J. Argod, P. Levy Characterization of pharyngeal resistance during sleep in a spectrum of sleep-disordered breathing", Journal of Applied Physiology 89:120-130, 2000.
5. Mansour K., Rowley J., Meshenish A., Shkoukani M., Badr M., A mathematical model to detect inspiratory flow limitation during sleep", J Appl Physiol 93:1084-1092, 2002
6. Morgenstern C., Jané R., Schwaibold M., Randerath W., Characterization of inspiratory flow limitation during sleep with an exponential model, IEEE EMBS, Proc. of EMBC 2008, admitted for publication
7. The Report of an American Academy of Sleep Medicine Task Force, Sleep-Related Breathing Disorders in Adults: Recommendations for Syndrome Definition and Measurement Techniques in Clinical Research, SLEEP, Vol. 22, No. 5, 1999
8. Chervin R., Aldrich M., Effects of Esophageal Pressure Monitoring on Sleep Architecture, AM J RESPIR CRIT CARE MED;156:881-885. 1997
9. Ayappa I., Norman RG, Krieger AC, Rosen A., O'Malley R., Rapoport D., Non-Invasive Detection of Respiratory Effort-Related Arousals (RERAs) by a Nasal Cannula/Pressure Transducer System, Sleep, Vol. 23, No. 6, 2000
10. K. F. Mansour, J. A. Rowley and M. S. Badr, "Noninvasive determination of upper airway resistance and flow limitation", J Appl Physiol, November 1, 97 (5): 1840-1848, 2004
11. T. Aittokallio, O. Nevalainen, U. Pursiheimo, T. Saaresranta, O. Polo, Classification of Nasal Inspiratory Flow Shapes by Attributed Finite Automata, Comps and Biomedical Research 32, 34-55 1999
12. Aittokallio T., Malminen J., Pahikkala T., Polo O., Nevalainen O., Inspiratory flow shape clustering: An automated method to monitor upper airway performance during sleep, Computer Methods and Programs in Biomedicine 85, 2007, 8-18
13. Heisele B., Riskov I., Morgenstern C., Components for Object Detection and Identification, in Towards Category-Level Object Recognition, Ponce J., Hebert M., Schmid C., Zisserman A., Springer-Verlag, (LNCS) Vol. 4170, 2006, XI, ISBN: 978-3-540-68794-8
14. Vapnik, V. The Nature of Statistical Learning Theory. Springer-Verlag, New York, 1995.
15. Freund Y, Schapire R. Game theory, on-line prediction and boosting. In Proc. 9th arm. Conf. on Comp. Learning Theory, pp. 325-332, 1996
16. Friedman J, Hastie T, Tibshirani R. Additive logistic regression: A statistical view of boosting. Annals of Statistics, 38(2):337-374, 2000.
17. Vezhnevets A. Vezhnevets V., Modest AdaBoost-teaching AdaBoost to generalize better. Graphicon 2005.