Abstract
The chapter introduces an automated system for prediction and detection of cardiac arrhythmias especially VT/VF. The system is overviewed, next the ECG signal processing specifics are covered, and then the feature extraction process is explained. The chapter is concluded by explaining features of the classification system.
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J. Zhang, P.V. Orlik, Z. Sahinoglu, A.F. Molisch, P. Kinney, UWB systems for wireless sensor networks. Proc. IEEE 97(2), 313–331 (2009)
A.L. Goldberger, L.A. Amaral, L. Glass, J.M. Hausdorff, P.C. Ivanov, R.G. Mark, J.E. Mietus, G.B. Moody, C.-K. Peng, H.E. Stanley, Physiobank, physiotoolkit, and physionet components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2000)
J. J. Nobel. (Online). Available: https://www.ecri.org/Products/Pages/AHAECGDVD.aspx
J. Pan, W.J. Tompkins, A real-time QRS detection algorithm. IEEE Trans. Biomed. Eng. 32(3), 230–236 (1985)
N. Bayasi, T. Tekeste, H. Saleh, A. Khandoker, B. Mohammad, M. Ismail, Adaptive technique for P and T wave delineation in electrocardiogram signals, in Engineering in Medicine and Biology Society, 2014. International Conference of the IEEE (2014)
T. Heeren, R. D’Agostino, Robustness of the two independent samples t-test when applied to ordinal scaled data. Stat. Med. 6(1), 79–90 (1987)
D.B. Panagiotakos, The value of p-value in biomedical research. Open Cardiovasc. Med. J. 2, 97 (2008)
R. Kumar, A. Indrayan, Receiver operating characteristic (ROC) curve for medical researchers. Indian Pediatr. 48(4), 277–287 (2011)
M.S. Finkler, Lab 10: Cardiovascular Physiology (Indian University), pp. 1–5
P. De Chazal, M. O’Dwyer, R.B. Reilly, Automatic classification of heartbeats using ECG morphology and heartbeat interval features. IEEE Trans. Biomed. Eng. 51(7), 1196–1206 (2004)
F. Alonso-Atienza, E. Morgado, L. Fernandez-Martinez, A. GarcÃa-Alberola, J. Rojo-Alvarez, Detection of life-threatening arrhythmias using feature selection and support vector machines. I.E.E.E. Trans. Biomed. Eng. 61(3), 832–840 (2014)
Q. Li, C. Rajagopalan, G. Clifford, Ventricular fibrillation and tachycardia classification using machine learning method. I.E.E.E. Trans. Biomed. Eng. 61(6), 1607–1613 (2013)
H. Wang, T.M. Khoshgoftaar, K. Gao, A comparative study of filter-based feature ranking techniques, in Information Reuse and Integration (IRI), 2010 IEEE International Conference on (IEEE, 2010), p. 43–48
G. Forman, An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res. 3, 1289–1305 (2003)
C. Pillers Dobler, Mathematical statistics: basic ideas and selected topics. Am. Stat. 56(4), 332–332 (2002)
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Saleh, H., Bayasi, N., Mohammad, B., Ismail, M. (2018). System Design and Development. In: Self-powered SoC Platform for Analysis and Prediction of Cardiac Arrhythmias . Analog Circuits and Signal Processing. Springer, Cham. https://doi.org/10.1007/978-3-319-63973-4_3
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DOI: https://doi.org/10.1007/978-3-319-63973-4_3
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