Segmentation, alignment and statistical analysis of biosignals with application to disease classification

Journal of Applied Statistics

Volumn 40, Issue 6, 2013, Pages 1270-1288

  Kurtek, Sebastian ^a   Wu, Wei ^b   Christensen, Gary E ^c   Srivastava, Anuj ^b  

^a OHIO STATE UNIVERSITY   (United States)

^b FLORIDA STATE UNIVERSITY   (United States)

^c University of Iowa   (United States)

Author keywords

cyclostationary biosignal segmentation; disease classification; functional data analysis; gait; myocardial infarction

Indexed keywords

EID: 84877839484     PISSN: 02664763     EISSN: 13600532     Source Type: Journal    
DOI: 10.1080/02664763.2013.785492     Document Type: Article

References (28)

1. American Heart Association, About Heart Attacks, 2011. Available at http://www.heart.org/HEARTORG/Conditions/HeartAttack/AboutHeartAttacks/About-Heart-Attacks_UCM_002038_Article.jsp
2. Arif, M., Malagore, I. A. and Afsar, F. A. 2012. Detection and localization of myocardial infarction using k-nearest neighbor classifier. J. Med. Syst., 36: 279 - 289. (doi:10.1007/s10916-010-9474-3)
3. Bousseljot, R., Kreiseler, D. and Schnabel, A. 1995. Nutzung der EKG-Signaldatenbank CARDIODAT der PTB uber das Internet. Biomedizinische Technik, 40: S317 - S318.
4. Čencov, N. N. 1982. " Statistical Decision Rules and Optimal Inferences ". In Translations of Mathematical Monographs, Vol. 53, Providence, RI: AMS.
5. Clifford, G. D., Azuaje, F. and McSharry, P. 2006. Advanced Methods and Tools for ECG Data Analysis, Boston, MA: Artech House, Inc.
6. Derawi, M., Bours, P. and Holien, K. Improved cycle detection for accelerometer based gait authentication. Sixth International Conference on Intelligent Information Hiding and Multimedia Signal Processing. pp. 312 - 317. Darmstadt, Germany
7. Engblom, H. and Pahlm, O. ECG-MRI based localization of myocardial infarction. Current News in Cardiology: Proceedings of the Mediterranean Cardiology Meeting 2007. May 20-22, Taormina, Italy. Edited by: Gulizia, M. M. pp. 347 - 354. Milan: Springer.
8. Gervini, D. and Gasser, T. 2004. Self-modeling warping functions. J. R. Stat. Soc. Ser. B, 66: 959 - 971. (doi:10.1111/j.1467-9868.2004.B5582.x)
9. Gohel, B. and Tiwary, U. 2010. Automated risk identification of myocardial infarction using relative frequency band coefficient (RFBC) features from ECG. Open Biomed. Eng. J., 4: 217 - 222. (doi:10.2174/1874120701004010217)
10. Gohel, B., Tiwary, U. and Lahiri, T. Relative amplitude based features of characteristic ECG-peaks for identification of coronary artery disease. First International Conference on Intelligent Human Computer Interaction. pp. 140 - 146. Allahabad, India
11. Goldberger, A. L., Amaral, L. A.N., Glass, L., Hausdorff, J. M., Ivanov, P. Ch., Mark, R. G., Mietus, J. E., Moody, G. B., Peng, C. K. and Stanley, H. E. 2000. PhysioBank, PhysioToolkit, and PhysioNet: Components of a new research resource for complex physiologic signals. Circulation, 101: e215 - e220. (doi:10.1161/01.CIR.101.23.e215)
12. Gudmundsson, S., Runarsson, T. and Sigurdsson, S. 2012. Test-retest reliability and feature selection in physiological time series classification. Comput. Meth. Program Biomed., 105: 55 - 60.
13. Hausdorff, J., Lertratanakul, A., Cudkowicz, M., Peterson, A., Kaliton, D. and Goldberger, A. 2000. Dynamic markers of altered gait rhythm in amyotrophic lateral sclerosis. J. Appl. Physiol., 88: 2045 - 2053.
14. Hausdorff, J., Mitchell, S., Firtion, R., Peng, C., Cudkowicz, M., Wei, J. and Goldberger, A. 1997. Altered fractal dynamics of gait: Reduced stride-interval correlations with aging and Huntington's disease. J. Appl. Physiol., 82: 262 - 269.
15. James, G. 2007. Curve alignment by moments. Ann. Appl. Stat., 1: 480 - 501. (doi:10.1214/07-AOAS127)
16. Kneip, A. and Gasser, T. 1992. Statistical tools to analyze data representing a sample of curves. Ann. Stat., 20: 1266 - 1305. (doi:10.1214/aos/1176348769)
17. Kneip, A. and Ramsay, J. O. 2008. Combining registration and fitting for functional models. J. Am. Stat. Assoc., 103: 1155 - 1165. (doi:10.1198/016214508000000517)
18. Kurtek, S., Srivastava, A. and Wu, W. Signal estimation under random time-warpings and nonlinear signal alignment. Neural Information Processing Systems (NIPS) Conference. Granda, Spain
19. Lahiri, T., Kumar, U., Mishra, H., Sarkar, S. and Roy, A. 2009. Analysis of ECG signal by chaos principle to help automatic diagnosis of myocardial infarction. J. Sci. Ind. Res., 68: 866 - 870.
20. Liu, X. and Müller, H. G. 2004. Functional convex averaging and synchronization for time-warped random curves. J. Am. Stat. Assoc., 99: 687 - 699. (doi:10.1198/016214504000000999)
21. Lu, H., Ong, K. and Chia, P. An automated ECG classification system based on a neuro-fuzzy system. Computers in Cardiology Conference. pp. 387 - 390. Cambridge, MA
22. Mäntyjärvi, J., Lindholm, M., Vildjiounaite, E., Mäkelä, S. and Ailisto, H. Identifying users of portable devices from gait pattern with accelerometers. IEEE International Conference on Acoustics, Speech, and Signal Processing. Philadelphia, PA
23. Mneimneh, M. and Povinelli, R. An electrophysiological cardiac model with applications to ischemia detection and infarction localization. Computers in Cardiology Conference. Park City, Utah
24. Ramsay, J. O. and Li, X. 1998. Curve registration. J. R. Stat. Soc. Ser. B, 60: 351 - 363. (doi:10.1111/1467-9868.00129)
25. Ramsay, J. O. and Silverman, B. W. 2005. Functional Data Analysis, 2, Springer Series in Statistics New York
26. Srivastava, A., Wu, W., Kurtek, S., Klassen, E. and Marron, J. S. 2011. Statistical analysis and modeling of elastic functions, ArXiv e-prints.
27. Tang, R. and Müller, H. G. 2008. Pairwise curve synchronization for functional data. Biometrika, 95: 875 - 889. (doi:10.1093/biomet/asn047)
28. Zheng, H., Wang, H., Nugent, C. and Finlay, D. Supervised classification models to detect the presence of old myocardial infarction in body surface potential maps. Computers in Cardiology Conference. Vol. 33, pp. 265 - 268. Valencia, Spain