A 12-lead clinical ECG classification method based on Semi-supervised Discriminant Analysis

In this paper, we propose an electrocardiogram (ECG) pattern classification method for 12-lead ECG using Semi-supervised Discriminant Analysis (SDA). The feature of 12-lead ECG signal is firstly extracted by wavelet transformation (WT). SDA is used to find a projection which projects the WT feature space into low dimension feature space for ECG pattern classification. The semi-supervised learning approach is used to cluster unlabeled data. Finally the SVM classifier is applied to multi-classification experiments. The experiment results show the proposed method can achieve high classification accuracy. When the labeled data is insufficient, the proposed method also demonstrates good generalization ability.

[1]  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.

[2]  Liqing Zhang,et al.  ECG Classification Using ICA Features and Support Vector Machines , 2011, ICONIP.

[3]  Keinosuke Fukunaga,et al.  Introduction to Statistical Pattern Recognition , 1972 .

[4]  Willis J. Tompkins,et al.  A Real-Time QRS Detection Algorithm , 1985, IEEE Transactions on Biomedical Engineering.

[5]  Marimuthu Palaniswami,et al.  Support Vector Machines for Automated Recognition of Obstructive Sleep Apnea Syndrome From ECG Recordings , 2009, IEEE Transactions on Information Technology in Biomedicine.

[6]  J. Jorgenson,et al.  Median filtering for removal of low-frequency background drift. , 1993, Analytical chemistry.

[7]  Lipo Wang,et al.  Data Mining With Computational Intelligence , 2006, IEEE Transactions on Neural Networks.

[8]  Chunru Wan,et al.  Classification using support vector machines with graded resolution , 2005, 2005 IEEE International Conference on Granular Computing.

[9]  Yean-Ren Hwang,et al.  ECG Feature Extraction and Classification Using Cepstrum and Neural Networks , 2008 .

[10]  Radford M. Neal.  Pattern Recognition and Machine Learning , 2007, Technometrics.

[11]  Mikhail Belkin,et al.  Laplacian Eigenmaps and Spectral Techniques for Embedding and Clustering , 2001, NIPS.

[12]  Jiawei Han,et al.  Semi-supervised Discriminant Analysis , 2007, 2007 IEEE 11th International Conference on Computer Vision.

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

[14]  Qi Li,et al.  A Chunking Method for Euclidean Distance Matrix Calculation on Large Dataset Using Multi-GPU , 2010, 2010 Ninth International Conference on Machine Learning and Applications.

[15]  D. Baudendistel.  Heart Disease A Textbook of Cardiovascular Medicine , 1993 .

[16]  David Burshtein,et al.  Support Vector Machine Training for Improved Hidden Markov Modeling , 2008, IEEE Transactions on Signal Processing.