论文信息 - UMPCA Based Feature Extraction for ECG

UMPCA Based Feature Extraction for ECG

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.

Liqing Zhang | Kai Huang | Dong Li | Hanlin Zhang

[1] Amnon Shashua,et al. Linear image coding for regression and classification using the tensor-rank principle , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2] J.B. Allen,et al. A unified approach to short-time Fourier analysis and synthesis , 1977, Proceedings of the IEEE.

[3] Christopher M. Bishop,et al. Pattern Recognition and Machine Learning (Information Science and Statistics) , 2006 .

[4] I. Jolliffe. Principal Component Analysis , 2002 .

[5] R. Saatchi,et al. Feature extraction and classification of electrocardiogram (ECG) signals related to hypoglycaemia , 2003, Computers in Cardiology, 2003.

[6] Hao Zhang,et al. ECG analysis based on PCA and Support Vector Machines , 2005, 2005 International Conference on Neural Networks and Brain.

[7] Chandan Chakraborty,et al. A two-stage mechanism for registration and classification of ECG using Gaussian mixture model , 2009, Pattern Recognit..

[8] Haiping Lu,et al. Uncorrelated Multilinear Principal Component Analysis for Unsupervised Multilinear Subspace Learning , 2009, IEEE Transactions on Neural Networks.

[9] Liqing Zhang,et al. ECG Feature Extraction and Classification Using Wavelet Transform and Support Vector Machines , 2005, 2005 International Conference on Neural Networks and Brain.

[10] Nasser M. Nasrabadi,et al. Pattern Recognition and Machine Learning , 2006, Technometrics.