Abstract
K-complex is a pattern which appears in the sleep EEG and characterizes the second stage of the NREM sleep. According to the underlying role of studying this pattern, we propose using synchrosqueezing transform (SST) for The purpose of analysis and automatic detection of K-complex. SST is an EMD-like time-frequency algorithm for signal analysis. Our idea is based on the robust properties of the SST and its previous satisfactory results on biomedical signals, especially those with specific patterns. We successfully applied SST on 10 segments of 30 minutes sleep EEG signals which contain K-complexes labeled by two experts. Results illustrate that SST representation is able to detect this pattern at the right time and frequency locations in the time frequency plane, which are in consistency with the standard definition. Comparison with the continuous wavelet demonstrates the superiority of SST especially in finding K-complexes at the right places, reducing the blurredness and mistakenly detecting other part of the signal as K-complex.
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