P01-Comparison of short-time Fourier transform and continuous wavelet transform for frequency analysis of sleep EEG

• Published in: Clinical neurophysiology 2018-04, Vol.129 (4), p.e14-e14
• Main Authors: Gerla, V., Saifutdinova, E., Macas, M., Mladek, A., Lhotska, L.
• Format: Article
• Language: English
• ISSN: 1388-2457; 1872-8952
• DOI: 10.1016/j.clinph.2018.01.046

We aimed to comparison of the most common methods for time-frequency analysis of biological signals, short-time Fourier Transform (STFT) and Continuous Wavelet Transform (CWT). This comparison was performed over sleep EEG data. We have acquired frequency-to-time graphs, spectrograms for STFT and scalograms for CWT. It has been confirmed that the results of STFT are significantly affected by the size of the sliding window through which the STFT is performed. The rapid changes in the EEG like movement artifacts were more noticeable when the window size was around 1–3 s. Conversely, the slow changes associated with deep sleep or sleep stage transitions were most pronounced with a window size over 60 s. This drawback disappears using the CWT method. We use CWT with the analytic Morse wavelet, symmetry parameter of 3 and a time-bandwidth product of 60. The resulting scalogram shows both faster and slower changes in EEG. Especially in the low-frequency range; it is possible to distinguish different deep sleep patterns. The described approaches may facilitate the visual evaluation of long-term sleep EEG recordings, or allow effective analysis of an unknown EEG signal structure. The research has been supported by the project No. 17-20480 of the GA CR.