Automatic non-invasive differentiation of obstructive and central hypopneas with nasal airflow compared to esophageal pressure

2010 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC'10

Volumn , Issue , 2010, Pages 6142-6145

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

^a BIOMATERIALES Y NANOMEDICINA CIBER BBN   (Spain)

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

^c Krankenhaus Bethanien Solingen   (Germany)

^d KARLSRUHE INSTITUTE OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

AIRFLOW PATTERNS; AUTOMATIC CLASSIFIERS; CLASSIFICATION ALGORITHM; CLINICAL ROUTINE; ESOPHAGEAL PRESSURE; HUMAN EXPERT; INSPIRATORY FLOW LIMITATIONS; INVASIVE TECHNIQUES; INVASIVENESS; NON-INVASIVE; POLYSOMNOGRAPHY; SLEEP-DISORDERED BREATHING; STANDARD METHOD; TIME ANALYSIS;

DISCRIMINANT ANALYSIS;

CLASSIFIERS;

ADULT; AGED; ALGORITHM; ARTICLE; AUTOMATION; CENTRAL SLEEP APNEA SYNDROME; COMPARATIVE STUDY; DIFFERENTIAL DIAGNOSIS; DISCRIMINANT ANALYSIS; ESOPHAGUS; FEMALE; HUMAN; LUNG VENTILATION; MALE; MIDDLE AGED; NOSE CAVITY; PATHOPHYSIOLOGY; PHYSIOLOGY; PRESSURE; SIGNAL PROCESSING; SLEEP DISORDERED BREATHING; YOUNG ADULT;

ADULT; AGED; ALGORITHMS; AUTOMATION; DIAGNOSIS, DIFFERENTIAL; DISCRIMINANT ANALYSIS; ESOPHAGUS; FEMALE; HUMANS; MALE; MIDDLE AGED; NASAL CAVITY; PRESSURE; PULMONARY VENTILATION; SIGNAL PROCESSING, COMPUTER-ASSISTED; SLEEP APNEA, CENTRAL; SLEEP APNEA, OBSTRUCTIVE; YOUNG ADULT;

EID: 78650847410     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IEMBS.2010.5627787     Document Type: Conference Paper

References (16)

1. Pack AI, Advances in Sleep Disordered Breathing, Am J Respir. Crit Care Med 172, 7-15, 2006
2. 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
3. Iber, C., Anoni-Israel S., Chesson A. L., Qua S.F., The AASM Manual for the Scoring of Sleep and Associated Events, American Academy of Sleep Medicine, Westchester, IL, 2007
4. O. Fontenla-Romero, B. Guijarro-Berdiñas, A. Alonso-Betanzos, V. Moret-Bonillo, A new method for sleep apnea classification using wavelets and feedforward neural networks, Artificial Intelligence in Medicine, 34:1, pp. 65-76, 2005
5. Steltner H, Staats R, Timmer J, Vogel M, Guttmann J, Matthys H, Virchow JC, Diagnosis of Sleep Apnea by Automatic Analysis of Nasal Pressure and Forced Oscillation Impedance, Am J Respir Crit Care Med Vol 165. pp 940-944, 2002
6. Yen FC, Behbehani K, Lucas EA, Burk JR, Axe JR, A Nonnvasive Technique for Detecting Obstructive and Central Sleep Apnea, IEEE Transactions on Biomedical Engineering, Vol. 44, No. 12, 1997
7. Argod J, Pépin JL, Lévy P., Differentiating Obstructive and Central Sleep Respiratory Events through Pulse Transit Time, Am J Respirat Crit Care Med 158:1778-1783. 1998
8. 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
9. Clark S. A., Wilson C. R., Sato M., Pegelow D., Dempsey J. A., Assessment of Inspiratory Flow Limitation Invasively and Noninvasively during Sleep, Am J Resp Crit Med, 158, 713-722, 1998
10. Morgenstern C., Schwaibold M., Randerath W., Bolz A., Jané R., An invasive and a non-invasive approach for the automatic differentiation of obstructive and central hypopneas, IEEE Trans. Biomed. Eng., in press, DOI: 10.1109/TBME.2010.2047505, 2010
11. Morgenstern C., Schwaibold M., Randerath W., Bolz A., Jané R., Assessment of changes in upper airway obstruction by automatic identification of inspiratory flow limitation during sleep, IEEE Trans. Biomed. Eng., Vol. 56 (8), pp. 2006-2015, 2009
12. Benditt, JO, Esophageal and Gastric Pressure Measurements, Respir Care, 50(1):68-75, 2005
13. Varady P, Micsik T, Benedek S, Benyo Z. A novel method for the detection of apnea and hypopnea events in respiration signals. IEEE Trans. Biomed Eng 49(9):936-942, 2002
14. T. Aittokallio, O. Nevalainen, U. Pursiheimo, T. Saaresranta, O. Polo, Classification of Nasal Inspiratory Flow Shapes by Attributed Finite Automata, Computers and Biomedical Research 32, 34-55, 1999
15. W. Randerath, C. Morgenstern, A. Oltmann, C. Priegnitz, M. Treml, N. Anduleit, Evaluation of a new PSG based algorithm to differentiate obstructive and central hypopnoeas, presented at the 20th Annual Conf. of the European Respiratory Society, Barcelona, Spain, 2010
16. Krzanowski, W. J., Principles of Multivariate Analysis, Oxford University Press, 1988