An Effective Way of J Wave Separation Based on Multilayer NMF

J wave is getting more and more important in the clinical diagnosis as a new index of the electrocardiogram (ECG) of ventricular bipolar, but its signal often mixed in normal ST segment, using the traditional electrocardiograph, and diagnosed by experience cannot meet the practical requirements. Therefore, a new method of multilayer nonnegative matrix factorization (NMF) in this paper is put forward, taking the hump shape J wave, for example, which can extract the original J wave signal from the ST segment and analyze the accuracy of extraction, showing the characteristics of hump shape J wave from the aspects of frequency domain, power spectrum, and spectral type, providing the basis for clinical diagnosis and increasing the reliability of the diagnosis of J wave.

[1]  Chong-Yung Chi,et al.  Blind Separation of Multichannel Biomedical Image Patterns by Non-negative Least-Correlated Component Analysis , 2006, PRIB.

[2]  Patrik O. Hoyer,et al.  Non-negative Matrix Factorization with Sparseness Constraints , 2004, J. Mach. Learn. Res..

[3]  Andrzej Cichocki,et al.  New Algorithms for Non-Negative Matrix Factorization in Applications to Blind Source Separation , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[4]  Kayvan Najarian,et al.  A Hierarchical Method for Removal of Baseline Drift from Biomedical Signals: Application in ECG Analysis , 2013, TheScientificWorldJournal.

[5]  Xu Chunyun An Blind Source Separation Algorithm Based on Constrained NMF , 2010 .

[6]  J. Daubert,et al.  Novel Brugada SCN5A Mutation Leading to ST Segment Elevation in the Inferior or the Right Precordial Leads , 2003, Journal of cardiovascular electrophysiology.

[7]  Dietrich Lehmann,et al.  Nonsmooth nonnegative matrix factorization (nsNMF) , 2006, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[8]  Cui Changcon Electrocardiographic J wave and J wave syndromes , 2004 .

[9]  Christian Jutten,et al.  Non-Negative Blind Source Separation Algorithm Based on Minimum Aperture Simplicial Cone , 2014, IEEE Transactions on Signal Processing.

[10]  Mark D. Plumbley Algorithms for nonnegative independent component analysis , 2003, IEEE Trans. Neural Networks.

[11]  Kazunori Yamaguchi,et al.  Linear Multilayer ICA Using Adaptive PCA , 2009, Neural Processing Letters.

[12]  Andrzej Cichocki,et al.  Multilayer Nonnegative Matrix Factorization Using Projected Gradient Approaches , 2007, Int. J. Neural Syst..

[13]  A. Cichocki,et al.  Multilayer nonnegative matrix factorisation , 2006 .

[14]  Hongyan Liu,et al.  The Application of FastICA Combined with Related Function in Blind Signal Separation , 2014 .