K-Complex Detection Based on Synchrosqueezing Transform

Document Type : Research Article

Authors

Faculty of Biomedical Engineering, Amirkabir University of Technology, Tehran, Iran

Abstract

K-complex is an underlying pattern in the sleep EEG. Due to the role of sleep studies in
neurophysiologic and cognitive disorders diagnosis, reliable methods for analysis and detection of this pattern
are of great importance. In our previous work, Synchrosqueezing Transform (SST) was proposed for analysis
of this pattern. SST is an EMD-like tool, which benefits from wavelet transform and reallocation approaches.
This method is able to decompose signals into their time-varying oscillatory ingredients. In addition, it
provides a time-frequency representation with less blurring compared to wavelet transform. In this paper,
firstly, the ability of SST is investigated by applying the ANOVA test, which is approved by proper p-values.
This paper proposes SST for K-complex detection. The proposed method is based on a so-called “detection
of K-complexes and sleep spindles” (DETOKS) framework. DETOKS is based on spares optimization
and decomposes signals into four components, namely transient, low frequency, oscillatory, and a residual.
Applying the Teager-Kaiser energy operator and setting a threshold on the low-frequency component result
in K-complex detection. We modify DETOKS using SST. The proposed method is applied to DREAMS
dataset. The dataset provides two visual scorings accompanied by an automatic one. As the visual labels were
extremely different, the automatic detection is considered as the third expert’s scoring and data is re-labeled
by a voting approach among three experts. For DETOKS, DETOKS modified by CWT, and the proposed
method, MCC measure is 0.62, 0.71, and 0.76, respectively. It shows superiority of the proposed method.

Keywords

Main Subjects

References

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AUT Journal of Electrical Engineering
Volume 49, Issue 2
December 2017
Pages 214-222

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