Background: This paper addresses the problem of detecting sleep spindles and K-complexes in human sleep EEG. Sleep spindles and K-complexes aid in classifying stage 2 NREM human sleep.
New method: We propose a non-linear model for the EEG, consisting of a transient, low-frequency, and an oscillatory component. The transient component captures the non-oscillatory transients in the EEG. The oscillatory component admits a sparse time-frequency representation. Using a convex objective function, this paper presents a fast non-linear optimization algorithm to estimate the components in the proposed signal model. The low-frequency and oscillatory components are used to detect K-complexes and sleep spindles respectively.
Results and comparison with other methods: The performance of the proposed method is evaluated using an online EEG database. The F1 scores for the spindle detection averaged 0.70 ± 0.03 and the F1 scores for the K-complex detection averaged 0.57 ± 0.02. The Matthews Correlation Coefficient and Cohen's Kappa values were in a range similar to the F1 scores for both the sleep spindle and K-complex detection. The F1 scores for the proposed method are higher than existing detection algorithms.
Conclusions: Comparable run-times and better detection results than traditional detection algorithms suggests that the proposed method is promising for the practical detection of sleep spindles and K-complexes.
Keywords: Convex optimization; K-complex detection; Sleep spindle detection; Sparse signal.
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