Classification of sleep apnea types using wavelet packet analysis of short-term ECG signals

Journal of Clinical Monitoring and Computing

Volumn 26, Issue 1, 2012, Pages 1-11

  Gubbi, Jayavardhana ^a   Khandoker, Ahsan ^a,b   Palaniswami, Marimuthu ^a  

^a UNIVERSITY OF MELBOURNE   (Australia)

^b KHALIFA UNIVERSITY   (United Arab Emirates)

Author keywords

Apnea detection; Central sleep apnea; Obstructive sleep apnea; Support vector machine; Wavelet packet analysis

Indexed keywords

APNEA DETECTION; BIORTHOGONAL WAVELET; CLASSIFICATION ACCURACY; CLASSIFICATION RESULTS; DAUBECHIES; DECOMPOSITION LEVEL; DISCRETE WAVELET ANALYSIS; ECG SIGNALS; GRID SEARCH; MOTHER WAVELETS; OBSTRUCTIVE SLEEP APNEA; SENSITIVITY AND SPECIFICITY; SLEEP APNEA; TRAINING PHASE; WAVELET BASIS FUNCTIONS; WAVELET PACKET; WAVELET PACKET ANALYSIS;

ALGORITHMS; ELECTROCARDIOGRAPHY; SLEEP RESEARCH; SUPPORT VECTOR MACHINES; WAVELET ANALYSIS; WAVELET DECOMPOSITION;

DISCRETE WAVELET TRANSFORMS;

ADULT; ALGORITHM; ARTICLE; CENTRAL SLEEP APNEA SYNDROME; CLASSIFIER; CLINICAL ARTICLE; CLIP; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; ELECTROCARDIOGRAM; HUMAN; POLYSOMNOGRAPHY; PRIORITY JOURNAL; SENSITIVITY AND SPECIFICITY; SLEEP APNEA SYNDROME; SUPPORT VECTOR MACHINE; WAVELET ANALYSIS; WAVELET PACKET ANALYSIS;

ELECTROCARDIOGRAPHY; HUMANS; MIDDLE AGED; POLYSOMNOGRAPHY; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SLEEP APNEA, CENTRAL; SLEEP APNEA, OBSTRUCTIVE; WAVELET ANALYSIS;

EID: 84863103530     PISSN: 13871307     EISSN: 15732614     Source Type: Journal    
DOI: 10.1007/s10877-011-9323-z     Document Type: Article

References (22)

1. Yen FC, Behbehani K, Lucas EA, Burk JR, Axe JR. A nonin-vasive technique for detecting obstructive and central sleep apnea. IEEE T Bio Med Eng. 1997;44:1262-8.
2. Bradley TD, Floras JS. Sleep apnea and heart failure: part I: obstructive sleep apnea. Circulation. 2003;107:1671-8. (Pubitemid 36397338)
3. Bradley TD, Floras JS. Sleep apnea and heart failure: part II: central sleep apnea. Circulation. 2003;107:1822-6. (Pubitemid 36418366)
4. White DP. Pathogenesis of obstructive and central sleep apnea. Am J Resp Crit Care. 2005;172:1363-70. (Pubitemid 43069808)
5. Penzel T, McNames J, De Chazal P, Raymond B, Murray A, Moody G. Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med Biol Eng Comput. 2002;40:402-7. (Pubitemid 34982531)
6. Zywietz CW, et al. ECG analysis for sleep apnea detection. Methods Inf Med. 2004;43(1):56-9. (Pubitemid 38387535)
7. Mendez MO, et al. Automatic screening of obstructive sleep apnea from the ECG based on empirical mode decomposition and wavelet analysis. Physiol Meas. 2010;31(3):273-89.
8. Tong GM, et al. Screening of sleep apnea syndrome by ECG derived respiration of ambulatory electrocardiogram. Zhonghua Yi Xue Za Zhi. 2006;86(22):1545-8.
9. Bsoul M, Minn H, Tamil L. Apnea MedAssist: real-time sleep apnea monitor using single-lead ECG. IEEE Trans Inf Technol Biomed. 2011;15(3):416-27.
10. Maier C, Dickhaus H. Central sleep apnea detection from ECG-derived respiratory signals. Application of multivariate recurrence plot analysis. Methods Inf Med. 2010;49(5):462-6.
11. Khandoker AH, Gubbi J, Palaniswami M. Automated scoring of obstructive sleep apnoea and hypopnea events using short term electrocardiogram recordings. IEEE T Inf Technol B. 2009;13: 1057-67.
12. Thomas RJ, Mietus JE, Peng C-K, et al. Differentiating obstructive from central and complex sleep apnea using and automated electrocardiogram-based method. Sleep. 2007;30:1756-69. (Pubitemid 350234754)
13. Khandoker AH, Palaniswami M. Modeling respiratory movement signals during central and obstructive sleep apnea events using electrocardiogram. Ann Biomed Eng. 2011;39(2):801-11.
14. Gubbi J, Khandoker AH, Palaniswami M. Classification of obstructive and central sleep apnea using wavelet packet analysis of ECG signals. In: A. Murray, editor. Computers in cardiology. IEEE Press, Par City, Utah, USA; 2009. p. 733-6.
15. American Academy of Sleep Medicine Task Force. Sleep-related breathing disorders in adults: recommendations for syndrome definition and measurement techniques in clinical research. Sleep. 1999;22:667-89.
16. Chan YT. Wavelet basics. 1st ed. Dordrecht: Kluwer; 1995.
17. David FW. An introduction to wavelet analysis. 1st ed. Boston: Birkhauser; 2002.
18. Jensen A, La Cour-Harbo A. Ripples in mathematics - discrete wavelet transforms. 1st ed. Heidelberg: Springer; 2001.
19. Shannon CE. A mathematical theory of communication. Bell Syst Tech J. 1948;27:379-423.
20. Vapnik V. Statistical learning theory. 1st ed. New York: Springer; 1995.
21. Burges CJC. A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov. 1998;2:121-67. (Pubitemid 128695475)
22. Joachims T. Making large-scale svm learningpractical. In: Scholkopf B, Burges CJC, Smola AJ, editors. Advances in Kernel methods. Support vector learning. Cambridge: MIT Press; 1999. p. 69-184.