A comparative approach to ECG feature extraction methods

Vaneghi, F.M. and Oladazimi, M. and Shiman, F. and Kordi, A. and Safari, M.J. and Ibrahim, F. (2012) A comparative approach to ECG feature extraction methods. In: 3rd International Conference on Intelligent Systems Modelling and Simulation, ISMS 2012, 2012, Kota Kinabalu.

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Abstract

This paper discusses six most frequent methods used to extract different features in Electrocardiograph (ECG) signals namely Autoregressive (AR), Wavelet Transform (WT), Eigenvector, Fast Fourier Transform (FFT), Linear Prediction (LP), and Independent Component Analysis (ICA). The study reveals that Eigenvector method gives better performance in frequency domain for the ECG feature extraction. © 2012 IEEE.

Item Type:	Conference or Workshop Item (Paper)
Funders:	UNSPECIFIED
Additional Information:	Conference code: 89401 Export Date: 29 January 2014 Source: Scopus Art. No.: 6169708 doi: 10.1109/ISMS.2012.35 Language of Original Document: English Correspondence Address: Vaneghi, F.M.; Medical Informatics and Biological Micro-electro-mechanical Systems (MIMEMS) Specialized Laboratory, Department of Biomedical Engineering, Faculty of Engineering University, Malaya, 50603, Kuala Lumpur, Malaysia; email: vistamasiha@gmail.com References: Clifford, G.D., (2006) Advanced Methods and Tools for ECG Data Analysis, , Artech House; Zhang, Z.G., (2004) Pattern Recognition of Cardiac Arrhythmias Using Scalar Autoregressive Modeling, 6, pp. 5545-5548; Li, N., Li, P., (2009) A Switching Method Based on FD and WTMM for ECG Signal Real-Time Feature Extraction, pp. 828-830; Mallat, S., Zero-crossings of a wavelet transform (1991) Information Theory, IEEE Transactions on, 37, pp. 1019-1033; Ubeyli, E., (2007) Eigenvector Methods for Analysis of Human PPG, ECG and EEG Signals, pp. 3304-3307; �beyli, E.D., Güler, I., Improving medical diagnostic accuracy of ultrasound Doppler signals by combining neural network models (2005) Computers in Biology and Medicine, 35, pp. 533-554; Maniewski, R., (1993) Time-frequency Methods for Highresolution ECG Analysis, 3, pp. 1266-1267; Tayel, M.B., El-Bouridy, M.E., (2008) ECG Images Classification Using Artificial Neural Network Based on Several Feature Extraction Methods, pp. 113-115; Challis, R., Kitney, R., Biomedical signal processing (in four parts) (1990) Medical and Biological Engineering and Computing, 28, pp. 509-524; Challis, R., Kitney, R., Biomedical signal processing (in four parts) (1991) Medical and Biological Engineering and Computing, 29, pp. 1-17; Kay, S.M., Marple Jr., S.L., Spectrum analysis-A modern perspective (1981) Proceedings of the IEEE, 69, pp. 1380-1419; Noponen, K., Electrocardiogram Quality Classification Based on Robust Best Subsets Linear Prediction Error.; Hyvarinen, A., Fast and robust fixed-point algorithms for independent component analysis (1999) Neural Networks, IEEE Transactions on, 10, pp. 626-634; Wang, Z., (1997) Blind EGG Separation Using ICA Neural Networks, 3, pp. 1351-1354; De Lathauwer, L., Fetal electrocardiogram extraction by blind source subspace separation (2000) Biomedical Engineering, IEEE Transactions on, 47, pp. 567-572; Vigário, R., Independent component approach to the analysis of EEG and MEG recordings (2000) Biomedical Engineering, IEEE Transactions on, 47, pp. 589-593; Owis, M., Characterisation of electrocardiogram signals based on blind source separation (2002) Medical and Biological Engineering and Computing, 40, pp. 557-564; Herrero, G.G., (2005) Feature Extraction for Heartbeat Classification Using Independent Component Analysis and Matching Pursuits, 4, pp. iv/725-iv/728; Hyvärinen, A., (2001) Independent Component Analysis, 26. , Wiley-interscience; Bell, A.J., Sejnowski, T.J., An informationmaximization approach to blind separation and blind deconvolution (1995) Neural Computation, 7, pp. 1129-1159; Cardoso, J.F., Laheld, B.H., Equivariant adaptive source separation (1996) Signal Processing, IEEE Transactions on, 44, pp. 3017-3030; Hyvärinen, A., Oja, E., A fast fixed-point algorithm for independent component analysis (1997) Neural Computation, 9, pp. 1483-1492; Yu, S.N., Chou, K.T., Selection of significant independent components for ECG beat classification (2009) Expert Systems with Applications, 36, pp. 2088-2096; Ge, D.F., Study of feature extraction based on autoregressive modeling in egg automatic diagnosis (2007) Acta Automática Sinica, 33, pp. 462-466; Sandercock, G.R.H., The reliability of short-term measurements of heart rate variability (2005) International Journal of Cardiology, 103, pp. 238-247; Chemla, D., Comparison of fast Fourier transform and autoregressive spectral analysis for the study of heart rate variability in diabetic patients (2005) International Journal of Cardiology, 104, pp. 307-313; Pitzalis, M.V., Short-and long-term reproducibility of time and frequency domain heart rate variability measurements in normal subjects (1996) Cardiovascular Research, 32, p. 226; Cardoso, J.F., Souloumiac, A., (1993) Blind Beamforming for Non-Gaussian Signals, pp. 362-370
Uncontrolled Keywords:	AR, ECG feature extraction, Eigenvector, FFT, ICA, LP, Wavelet, Auto-regressive, Comparative approach, Eigenvector methods, Fast Fourier transform (FFT), Feature extraction methods, Frequency domains, Linear prediction, Argon, Eigenvalues and eigenfunctions, Fast Fourier transforms, Feature extraction, Independent component analysis, Intelligent systems, Electrocardiography
Subjects:	T Technology > T Technology (General) T Technology > TA Engineering (General). Civil engineering (General)
Divisions:	Faculty of Engineering
Depositing User:	Mr Jenal S
Date Deposited:	12 Feb 2014 01:20
Last Modified:	01 Nov 2017 05:37
URI:	http://eprints.um.edu.my/id/eprint/9269

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