Abstract
In this paper, we propose an algorithm for 12-leads ECG signals feature extraction by Uncorrelated Multilinear Principal Component Analysis(UMPCA). However, traditional algorithms usually base on 2-leads ECG signals and do not efficiently work out for 12-leads signals. Our algorithm aims at the natural 12-leads ECG signals. We firstly do the Short Time Fourier Transformation(STFT) on the raw ECG data and obtain 3rd-order tensors in the spatial-spectral-temporal domain, then take UMPCA to find a Tensor-to-Vector Projection(TVP) for feature extraction. Finally the Support Vector Machine(SVM) classifier is applied to achieve a high accuracy with these features.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Zhao, Q.B., Zhang, L.Q.: ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines. In: International Conference on Neural Networks and Brain, pp. 1089–1092 (2005)
Alexakis, C., Nyongesa, H.O., Saatchi, R., Harris, N.D., Davies, C., Emery, C., Ireland, R.H., Heller, S.R.: Feature Extraction and Classification of Electrocardiogram(ECG) Signals Related to Hypoglycaemia. In: Conference on Computers in Cardiology, pp. 537–540 (2003)
Zhang, H., Zhang, L.Q.: ECG analysis based on PCA and Support Vector Machines. In: International Conference on Neural Networks and Brain, pp. 743–747 (2005)
Martis, R.J., Chakraborty, C., Ray, A.K.: A two-stage mechanism for registration and classification of ECG using Gaussian mixture model. Pattern Recognition 42(11), 2979–2988 (2009)
Allen, J.B., Rabiner, L.R.: A Unified Approach to Short-Time Fourier Analysis and Synthesis. Proceedings of IEEE 65(11), 1558–1564 (1977)
Lu, H., Plataniotis, K.N., Venetsanopoulos, A.N.: Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning. IEEE Transactions on Neural Networks 20, 1820–1836 (2009)
Shashua, A., Levin, A.: Linear image coding for regression and classification using the tensor-rank principle. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 42–49 (2001)
Jolliffe, I.T.: Principal Component Analysis, 2nd edn. Springer Serires in Statistics (2002)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer-Verlag New York (2006)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Li, D., Huang, K., Zhang, H., Zhang, L. (2013). UMPCA Based Feature Extraction for ECG. In: Guo, C., Hou, ZG., Zeng, Z. (eds) Advances in Neural Networks – ISNN 2013. ISNN 2013. Lecture Notes in Computer Science, vol 7951. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-39065-4_47
Download citation
DOI: https://doi.org/10.1007/978-3-642-39065-4_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-39064-7
Online ISBN: 978-3-642-39065-4
eBook Packages: Computer ScienceComputer Science (R0)