<abstract xmlns="http://www.w3.org/1999/xhtml"><p>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 K-complex. This paper comprehend the developing of two algorithms in order to achieve an automatic K-complex detection from EEG raw data. These algorithms are based on a time-frequency analysis and two time-frequency techniques, the Short Time Fourier Transform (STFT) and 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 first algorithm is based on the energy distribution of the CWT detecting the spectral component of the K-complex. The second algorithm is focused on the morphology of the K-complex / sleep spindle waveform after the CWT. Evaluating the algorithms results reveals that a false K-complex detection is as important as real K-complex detection.</p></abstract>

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 K-complex. This paper comprehend the developing of two algorithms in order to achieve an automatic K-complex detection from EEG raw data. These algorithms are based on a time-frequency analysis and two time-frequency techniques, the Short Time Fourier Transform (STFT) and 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 first algorithm is based on the energy distribution of the CWT detecting the spectral component of the K-complex. The second algorithm is focused on the morphology of the K-complex / sleep spindle waveform after the CWT. Evaluating the algorithms results reveals that a false K-complex detection is as important as real K-complex detection.

K - Complex Detection Using the Continuous Wavelet Transform

ARS Medica Tomitana

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"K - Complex Detection Using the Continuous Wavelet Transform"}

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...

K - Complex Detection Using the Continuous Wavelet Transform

Publié en ligne: 15 avr. 2019

Pages: 144 - 152

DOI: https://doi.org/10.2478/arsm-2018-0031

Mots clés
EEG, K-complex, Short Time Fourier Transform, Continous Wavelet Transform, wave morphology

© 2018 Dumitrescu Cătălin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

K - Complex Detection Using the Continuous Wavelet Transform

Publié en ligne: 15 avr. 2019

Pages: 144 - 152

DOI: https://doi.org/10.2478/arsm-2018-0031

Mots clésEEG, K-complex, Short Time Fourier Transform, Continous Wavelet Transform, wave morphology

© 2018 Dumitrescu Cătălin et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
EEG, K-complex, Short Time Fourier Transform, Continous Wavelet Transform, wave morphology