Commenced in January 2007
Paper Count: 31584
Sparse Coding Based Classification of Electrocardiography Signals Using Data-Driven Complete Dictionary Learning
Abstract:In this paper, a data-driven dictionary approach is proposed for the automatic detection and classification of cardiovascular abnormalities. Electrocardiography (ECG) signal is represented by the trained complete dictionaries that contain prototypes or atoms to avoid the limitations of pre-defined dictionaries. The data-driven trained dictionaries simply take the ECG signal as input rather than extracting features to study the set of parameters that yield the most descriptive dictionary. The approach inherently learns the complicated morphological changes in ECG waveform, which is then used to improve the classification. The classification performance was evaluated with ECG data under two different preprocessing environments. In the first category, QT-database is baseline drift corrected with notch filter and it filters the 60 Hz power line noise. In the second category, the data are further filtered using fast moving average smoother. The experimental results on QT database confirm that our proposed algorithm shows a classification accuracy of 92%.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1126027Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1729
 Di Marco, L.Y. and L. Chiari, A wavelet-based ECG delineation algorithm for 32-bit integer online processing. BioMedical Engineering OnLine, 2011. 10(23): p. 19.
 Baali, H. and M. Mesbah. Ventricular ectopic beats classification using Sparse Representation and Gini Index. in Engineering in Medicine and Biology Society (EMBC), 2015 37th Annual International Conference of the IEEE. 2015.
 Lee, S., J. Luan, and P.H. Chou. A new approach to compressing ECG signals with trained overcomplete dictionary. in Wireless Mobile Communication and Healthcare (Mobihealth), 2014 EAI 4th International Conference on. 2014.
 Lee, S.J., J. Luan, and P.H. Chou, ECG signal reconstruction from undersampled measurement using a trained overcomplete dictionary. Contemporary Engineering Sciences, Vol. 7, 2014, no. 29, 1625 - 1632 HIKARI Ltd, 2014.
 Mathews, S.M., L.F. Polanıa, and K.E. Barner. Leveraging a discriminative dictionary learning algorithm for single-lead ECG classification. in Biomedical Engineering Conference (NEBEC), 2015 41st Annual Northeast. 2015.
 Tseng, Y.L., K.S. Lin, and F.S. Jaw, Comparison of Support-Vector Machine and Sparse Representation Using a Modified Rule-Based Method for Automated Myocardial Ischemia Detection. Comput Math Methods Med, 2016. 2016: p. 9460375.
 Li, Q., C. Rajagopalan, and G.D. Clifford, Ventricular Fibrillation and Tachycardia Classification Using a Machine Learning Approach. IEEE Transactions on Biomedical Engineering, 2014. 61(6): p. 1607-1613.
 Adler, A., et al., Sparse Coding with Anomaly Detection. Journal of Signal Processing Systems, 2014. 79(2): p. 179-188.
 Wang Tian, J., Y. Zheng Bao, and Z. Yang. An overcomplete dictionary design algorithm for sparse representation of piecewise stationary signals. in Communications (APCC), 2012 18th Asia-Pacific Conference on. 2012.
 Laguna, P., et al. A database for evaluation of algorithms for measurement of QT and other waveform intervals in the ECG. in Computers in Cardiology 1997. 1997.
 Tropp, J.A., Greed is good: algorithmic results for sparse approximation. IEEE Transactions on Information Theory, 2004. 50(10): p. 2231-2242.
 Seghouane, A.K. and M. Hanif. A sequential dictionary learning algorithm with enforced sparsity. in Acoustics, Speech and Signal Processing (ICASSP), 2015 IEEE International Conference on. 2015.
 Elad, M., Sparse and Redundant Representations: From Theory to Applications in Signal and Image Processing. 2010: Springer Publishing Company, Incorporated. 376.
 Mohebbi, M. and H.A. Moghadam. An Algorithm for Automated Detection of Ischemic ECG Beats Using Support Vector Machines. in Signal Processing and Communications Applications, 2007. SIU 2007. IEEE 15th. 2007.