Application of the wavelet transform in machine-learning

The wide variety of waveform in EEG signals and the high non-stationary nature of many of them is one of the main difficulties to develop automatic detection system for them. In sleep stage classification a relevant transient wave is the Kcomplex. The present paper purposes the developing an algorithms in order to achieve an automatic K-complex detection from EEG raw data. The algorithm is based on a time-frequency analysis and two time-frequency techniques, the Continuous Wavelet Transform (CWT), are tested in order to find out which one is the best for our purpose, being of two wavelet functions to measure the capability of them to detect K-complex and to choose one to be employed in the algorithms. The algorithm is based on the energy distribution of the CWT detecting the spectral component of the K-complex.