Apnea MedAssist: Real-time sleep apnea monitor using single-lead ECG

IEEE Transactions on Information Technology in Biomedicine

Volumn 15, Issue 3, 2011, Pages 416-427

  Bsoul, Majdi ^a,b   Minn, Hlaing ^c   Tamil, Lakshman ^c  

^a NOKIA RESEARCH CENTER   (United States)

^b Quality of Life Technology Laboratory   (United States)

^c The University of Texas at Dallas   (United States)

Author keywords

Apnea monitor; ECG; home care; smartphone; support vector machines (SVMs)

Indexed keywords

APNEA MONITOR; AUTOMATED SYSTEMS; CLINICAL CARE; ECG; F-MEASURE; FEATURE EXTRACTION AND CLASSIFICATION; FEATURE SETS; HIGH DEGREE OF ACCURACY; HOME CARE; MODEL COMPLEXITY; MONITORING SYSTEM; OBSTRUCTIVE SLEEP APNEA; OPERATING SYSTEMS; REDUCED COMPLEXITY; RESPIRATION SIGNALS; SINGLE CHANNELS; SLEEP APNEA; SMARTPHONE; SMARTPHONES; SUPPORT VECTOR; SUPPORT VECTOR CLASSIFIERS;

AUTOMATION; ELECTROCHROMIC DEVICES; FEATURE EXTRACTION; SIGNAL ENCODING; SLEEP RESEARCH; SUPPORT VECTOR MACHINES; VECTORS;

ELECTROCARDIOGRAPHY;

ADULT; ALGORITHM; ARTICLE; ELECTROCARDIOGRAPHY; FEMALE; HUMAN; MALE; METHODOLOGY; PATHOPHYSIOLOGY; POLYSOMNOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING; SLEEP APNEA SYNDROME;

ADULT; ALGORITHMS; ELECTROCARDIOGRAPHY; FEMALE; HUMANS; MALE; POLYSOMNOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SIGNAL PROCESSING, COMPUTER-ASSISTED; SLEEP APNEA SYNDROMES;

EID: 79955648533     PISSN: 10897771     EISSN: None     Source Type: Journal    
DOI: 10.1109/TITB.2010.2087386     Document Type: Article

References (36)

1. W. T. McNicholas and P. Levy, "Sleep-related breathing disorders: Definitions and measurements", Eur. Respir. J., vol. 15, no. 6, pp. 988-989, 2000. (Pubitemid 30399264)
2. American Academy of Sleep Medicine (AASM) Task Force, "Sleeprelated breathing disorders in adults: Recommendations for syndrome definition and measurement techniques in clinical research", Sleep, vol. 22, pp. 667-689, 1999.
3. R. K. Kakkar and R. B. Berry, "Positive airway pressure treatment for obstructive sleep Apnea", Chest, vol. 132, pp. 1057-1072, 2007.
4. C. den Herder, J. Schmech, D. Appelboom, and N. de Vries, "Risks of general anaesthesia in people with obstructive sleep Apnea", Br. Med. J., (BMJ), vol. 329, pp. 955-959, 2004, 2011. (Pubitemid 39422912)
5. C. Cortes and V. Vapnik, "Support-vector machines", Mach. Learning, vol. 20, pp. 273-297, 1995.
6. V. Vapnik, The Nature of Statistical Learning Theory. New York: Springer-Verlag, 1995.
7. G. B. Moody, R. G. Mark, A. Zoccola, and S. Mantero, "Derivation of respiratory signals from multilead ECGs", Comput. Cardiol., vol. 12, pp. 113-116, 1985. (Pubitemid 17485676)
8. N. Oliver and F. Flores-Mangas, "HealthGear: A real-time wearable system for monitoring and analyzing physiological signals", in Proc. IEEE Int. Workshop Wearable Implantable Body Sens. Netw. (BSN), Apr. 2006, p. 4.
9. Y. Fu-Chung, K. Behbehani, E. Lucas, J. Burk, and J. Axe, "A noninvasive technique for detecting obstructive and central sleep Apnea", IEEE Trans. Biomed. Eng., vol. 44, no. 12, pp. 1262-1268, Dec. 1997.
10. A. H. Khandoker, M. Palaniswami, and C Karmakar, "Support vector machines for automated recognition of obstructive sleep Apnea syndrome from ECG recordings", IEEE Trans. Inf. Technol. Biomed., vol 13, no. 1, pp. 37-48, Jan. 2009.
11. A. Patangay, P. Vemuri, and A. Tewfik, "Monitoring of obstructive sleep Apnea in heart failure patients", in Proc. 29th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBS) Citï 1/2 Int., Lyon, France, Aug. 2007, pp. 1043-1046.
12. B. Raymond, R. M. Cayton, R. A. Bates, and M. J. Chappell, "Screening for obstructive sleep Apnea based on the electrocardiogram - The computers in cardiology challenge", Comput. Cardiol., vol. 27, pp. 267-270, 2000. (Pubitemid 32188142)
13. P. de Chazal, C. Heneghan, E. Sheridan, R. Reilly, P. Nolan, and M. O'Malley, "Automated processing of the single-lead electrocardiogram for the detection of obstructive sleep Apnea", IEEE Trans. Biomed. Eng., vol. 50, no. 6, pp. 686-696, Jun. 2003. (Pubitemid 36723602)
14. HTC Corporation, G1 Specifications. (2009). [Online]. Available: http://www.htc.com/.
15. Android Open Source Project. (2009). [Online]. Available: http://www.android.com/.
16. T. Penzel, "The Apnea-ECG database", Comput. Cardiol., vol. 27, pp. 255-258, 2000.
17. G. B. Moody, R. G. Mark, A. L. Goldberger, and T. Penzel, "Stimulating rapid research advances via focused competition: The computers in cardiology challenge", Comput. Cardiol., vol. 27, pp. 207-210, 2000. (Pubitemid 32188157)
18. M. Jansen and P Oonincx, Second Generation Wavelets and Applications. New York: Springer-Verlag, 2005.
19. C. S. Lee, C. K. Lee, and K. Y. Yoo, "New lifting based structure for undecimated wavelet transform", IEEE Electron. Lett., vol. 36, no. 22, pp. 1894-1895, Oct. 2000. (Pubitemid 32020521)
20. P. Hamilton and W. Tompkins, "Quantitative investigation of QRS detection rules using the MIT/BIH arrhythmia database", IEEE Trans. Biomed. Eng., vol. BME-33, no. 12, pp. 1157-1165, Dec. 1986. (Pubitemid 17517265)
21. G. D. Furman, Z. Shinar, A. Baharav, and S. Akselrod, "Electrocardiogram derived respiration during sleep", Comput. Cardiol., vol. 32, pp. 351-354, 2005. (Pubitemid 46276671)
22. F. Yasuma and J. Hayano, "Respiratory sinus arrhythmia", Amer. College Chest Physicians, Chest, vol. 125, pp. 683-690, 2004.
23. Y. Yoshida, K. Yokoyama, and N. Ishii, "Real-Time continuous estimation of respiratory frequency during sleep based on heart rate time series", in Proc. 29th Annu. Int. Conf. Eng. Med. Biol. Soc. (EMBS), Aug. 2007, pp. 648-651.
24. M. Teich, S. B. Lowen, B. M. Jost, K. Vibe-Rheymer, and C. Heneghan, "Heart-rate variability: Measures models", in Nonlinear Biomedical Signal Processing, M. Akay, Ed. Piscataway, NJ: IEEE Press, 2001, ch. 6, pp. 159-213.
25. G. D. Clifford, Advanced Methods & Tools for ECG Data Analysis. London, U. K.: Artech House, 2006.
26. A. Aldroubi and M. Unser, Wavelets in Medicine and Biology. Boca Raton, FL: CRC-Press, 1996.
27. S. R. Gunn, "Support vector machines for classification and regression", University of Southhampton, Southampton, U. K., Tech. Rep., 1998.
28. C. Chang and C. Lin. (2001). "LIBSVM: A library for support vector machines", [Online]. Available: http://www.csie.ntu.edu.tw/?cjlin/libsvm.
29. R. Kohavi, "A study of cross-validation and bootstrap for accuracy estimation and model selection", in Proc. Int. Joint Conf. Artif. Intell. (IJCAI), 1995, pp. 1137-1143.
30. C. J. van Rijsbergen, Information Retrieval, 2nd ed. London, U. K.: Butterworth-Heinemann Press, 1979.
31. Y. Chen and C. Lin, Combining SVMs with Various Feature Selection Strategies. New York: Springer-Verlag, 2006.
32. J. Hanley and B. McNeil, "Amethod of comparing the areas under receiver operating characteristic curves derived from the same cases", Radiology, vol. 148, pp. 839-843, 1983. (Pubitemid 13033817)
33. A. Ben-Hur, D. Horn, H. Siegelmann, and V. Vapnik, "Support vector clustering", J. Mach. Learning Res., vol. 2, pp. 125-137, 2001.
34. Q. Tran, Q. Zhang, and X. Li, "Reduce the number of support vectors by using clustering techniques", in Proc. 2nd Int. Conf. Mach. Learning Cybern., Xi'an, Nov. 2003, pp. 1245-1248.
35. S. Keerthi and C. Lin, "Asymptotic behaviors of support vector machines with Gaussian kernel", Neural Comput., vol. 15, no. 7, pp. 1667-1689, 2003. (Pubitemid 37035565)
36. J. Wang, X. Wu, and C. Zhang, "Support vector machines based on K-means clustering for real-time business intelligence systems", Int. J. Bus. Intell. Data Mining, vol. 1, no. 1, pp. 54-64, 2005. (Pubitemid 43796396)