Multiscale Two Directional Two Dimensional Principal Component Analysis and Its Application to High Dimensional Biomedical Signal Classification
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 a few signal channels. The steady improvement of biomedical recording techniques has increasingly permitted the registration of a high number of channels. However, WT PCA is not applicable to high dimensional recordings due to the curse of dimensionality and small sample size problem. In this study, we present a multiscale two directional 2 D principal component analysis method for the efficient and effective extraction of essential feature information from high dimensional signals. Multiscale matrices constructed in the first step incorporate the spatial correlation and physiological characteristics of subband signals among channels. In the second step, the two directional 2 D PCA operates on the multiscale matrices to reduce the dimension, rather than vectors in conventional PCA. Results are presented from an experiment to classify 20 hand movements using 89 channel EMG signals recorded in stroke survivors, which illustrates the efficiency and effectiveness of the proposed method for a high dimensional biomedical signal analysis.
IEEE Transactions on Biomedical Engineering