Data mining in ECG data

Dimitrios Tseles, Costas Alafodimos, Soultana Vasileiadou, Gregory Nikolaou

Abstract


: Electrocardiography (ECG) is a data source that can contain valuable knowledge for the body function and can reveal information for several diseases. It has been used across the years to diagnose cardiovascular diseases and to prognose upcoming abnormalities. Along with the evolution of data processing methods, data mining algorithms in ECG data have been used in the last three decades. This work investigates this area of research with the aim to realize the advances through time and present current research trend. The area of data mining is first presented and a brief introduction of the main algorithms is given. The focus of this work is placed on the different frameworks and models used in the area, namely signal processing approaches, morphological based processing and data mining. Literature shows a trend towards real-time diagnosis based on portable devices. Technology evolution provides portable computational power that can be used for real-time data processing and a number of research studies in this topic are presented. This work concludes with some remarks in this area and its future evolvement.

 


Keywords


Medical Data Mining, ECG Data, Arrhythmia, Real-time diagnosis

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References


I. Yoo, P. Alafaireet, M. Marinov, K. Pena-Hernandez, R. Gopidi, J.-F. Chang, and L. Hua, “Data Mining in Healthcare and Biomedicine: A Survey of the Literature,” J. Med. Syst., vol. 36, no. 4, pp. 2431–2448, 2012.

D. Hand, H. Mannila, and P. Smyth, Principles of data mining. 2001.

B. E. Boser, I. M. Guyon, and V. N. Vapnik, “A Training Algorithm for Optimal Margin Classifiers,” Proc. 5th Annu. ACM Work. Comput. Learn. Theory, pp. 144–152, 1992.

U. Rajendra Acharya, K. Paul Joseph, N. Kannathal, C. M. Lim, and J. S. Suri, “Heart rate variability: a review,” Med. Biol. Eng. Comput., vol. 44, no. 12, pp. 1031–1051, 2006.

J. Park, K. Lee, and K. Kang, “Arrhythmia Detection from Heartbeat Using k-nearest neighbor clasifier,” in Bioinformatics and Biomedicine (BIBM), IEEE International Conference on, 2013.

D. Cantzos, D. Dimogianopoulos, and D. Tseles, “ECG Diagnosis via a Sequential Recursive Time Series – Wavelet Classification Scheme,” in EUROCON, IEEE, 2013, no. July, pp. 1770–1777.

D. Ge, N. Srinivasan, and S. M. Krishnan, “Cardiac arrhythmia classification using autoregressive modeling.,” Biomed. Eng. Online, vol. 1, p. 5, 2002.

S. Karimifard, a Ahmadian, M. Khoshnevisan, and M. S. Nambakhsh, “Morphological heart arrhythmia detection using Hermitian basis functions and kNN classifier.,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 1, no. 4, pp. 1367–70, 2006.

C. Ye, M. T. Coimbra, and B. K. Vijaya Kumar, “Arrhythmia detection and classification using morphological and dynamic features of ECG signals.,” Conf. Proc. IEEE Eng. Med. Biol. Soc., vol. 2010, pp. 1918–1921, 2010.

H. a. Guvenir, B. Acar, G. Demiroz, and a. Cekin, “A supervised machine learning algorithm for arrhythmia analysis,” Comput. Cardiol. 1997, vol. 24, pp. 433–436, 1997.

M. G. Tsipouras, C. Voglis, I. E. Lagaris, and D. I. Fotiadis, “Cardiac arrhythmia classification using support vector machines,” in The 3rd European Medical and Biological Engineering Conference, 2005, pp. 2–7.

C. Voglis and I. E. Lagaris, “Boxcqp : an Algorithm for Bound Constrained Convex Quadratic Problems,” in International Conference from Schientific Computing to Computational Engineering, 2004, no. September, pp. 8–10.

T. Tang and P. Wang, “A Comparative Study of Medical Data Classification Methods Based on Decision Tree and System Reconstruction Analysis,” Ind. Eng. Manag. Syst., vol. 4, no. 1, pp. 102–108, 2005.

J. Pan and W. J. Tompkins, “A real-time QRS detection algorithm.,” IEEE Trans. Biomed. Eng., vol. 32, no. 3, pp. 230–236, 1985.

J. Rodríguez, A. Goñi, and A. Illarramendi, “Real-time classification of ECGs on a PDA,” IEEE Trans. Inf. Technol. Biomed., vol. 9, no. 1, pp. 23–34, 2005.

J. J. Oresko, H. Duschl, and A. C. Cheng, “A wearable smartphone-based platform for real-time cardiovascular disease detection via electrocardiogram processing.,” IEEE Trans. Inf. Technol. Biomed., vol. 14, no. 3, pp. 734–40, May 2010.


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