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- W88697330 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." @default.
- W88697330 created "2016-06-24" @default.
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- W88697330 date "2013-01-01" @default.
- W88697330 modified "2023-09-26" @default.
- W88697330 title "UMPCA Based Feature Extraction for ECG" @default.
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- W88697330 doi "https://doi.org/10.1007/978-3-642-39065-4_47" @default.
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