Feature extraction of signals plays an important role in classification problems because of data dimension reduction property and potential improvement of a classification accuracy rate. Principal component analysis (PCA), wavelets transform or Fourier transform methods are often used for feature extraction. In this paper, we propose a multi-scale PCA, which combines discrete wavelet transform, and PCA for feature extraction of signals in both the spatial and temporal domains. Our study shows that the multi-scale PCA combined with the proposed new classification methods leads to high classification accuracy for the considered signals.
 M. S. Taqqu, V. Teverovsky and W. Willinger, “Is Network Traffic Self-Similar or Multifractal?” Fractals, Vol. 5, 1997, pp. 63-74. http://dx.doi.org/10.1142/S0218348X97000073
 B. Vidakovi, “Statistical Modeling by Wavelets,” John Wiley & Sons, Inc., Hoboken, 1999. http://dx.doi.org/10.1002/9780470317020
 D. Donoho and I. Johnstone, “Minimax Estimation via Wavelet Shrinkage,” Annals of Statistics, Vol. 26, 1998, pp. 879-921. http://dx.doi.org/10.1214/aos/1024691081
 R. G. Andrzejak, K. Lehnertz, F. Mormann, C. Rieke, P. David and C. E. Elger, “Indications of Nonlinear Deterministic and Finite-Dimensional Structures in Time Series of Brain Electrical Activity: Dependence on Recording Region and Brain State,” Physical Review E, Vol. 64, No. 6, 2001, p. 6190. http://dx.doi.org/10.1103/PhysRevE.64.061907