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In this study, a stationary wavelet-based two-dimensional two-directional PCA (SW2D2PCA) approach is presented to efficiently and effectively extract essential feature information from signals for electromyographic (EMG) signal classification. First, multi-scale time-invariant wavelet coefficient matrices are obtained using stationary wavelet decomposition. Then, we employ 2D2PCA to reduce the size...
Goal: Time–frequency analysis incorporating the wavelet transform followed by the principal component analysis (WT-PCA) has been a powerful approach for the analysis of biomedical signals, such as electromyography (EMG), electroencephalography, electrocardiography, and Doppler ultrasound. Time–frequency coefficients at various scales were usually transformed into a 1-D array using only a single or...
The extraction method of classification feature is primary and core problem in all epileptic EEG detection algorithms, since it can seriously affect the performance of the detection algorithm. In this paper, a novel epileptic EEG feature extraction method based on the statistical parameter of weighted complex network is proposed. The EEG signal is first transformed into weighted network and the weight...
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