Abstract: Dimensionality reduction with two methods, namely, Laplacian Eigenmap (LE) and Locality Preserving Projections (LPP) is studied for normal and pathological noisy and noiseless ECG patterns. Besides, the possibility of using compressed sensing (CS) as a method of dimensionality reduction is also analyzed. The classification rate for the initial domain as well as in manifolds of various dimensions for the three cases are presented and compared.

Monica Fira and Liviu Goras, “On Some Methods for Dimensionality Reduction of ECG Signals” International Journal of Advanced Computer Science and Applications(IJACSA), 10(9), 2019. http://dx.doi.org/10.14569/IJACSA.2019.0100942

@article{Fira2019,
title = {On Some Methods for Dimensionality Reduction of ECG Signals},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2019.0100942},
url = {http://dx.doi.org/10.14569/IJACSA.2019.0100942},
year = {2019},
publisher = {The Science and Information Organization},
volume = {10},
number = {9},
author = {Monica Fira and Liviu Goras}
}