Abstract
Sleep spindles are thought to be related to some sleep diseases and play an important role in memory consolidation. They were traditionally identified by physiology experts based on rules and recently detected by automatic algorithms. However, many automatic approaches were validated on the different electroencephalogram (EEG) using various assessment methods, making it difficult to appraised a method objectively and fairly. In this paper, we proposed a sliding window-based probability estimation (SWPE) method for sleep spindle detection. We performed a continuous wavelet transform with Mexican hat wavelet function, following by a sliding window to find out the candidate spindle points corresponding to the large wavelet coefficients at the frequencies of spindles and estimated their probabilities. To enhance the results, we used the envelope of the rectified signal to reject some false sleep spindle candidates. This was an enhanced method and we called it SWPE-E in this paper. Finally, we compared our approaches with four approaches on the same public available EEG database, and the result showed the significative improvement of our proposed approaches.
Highlights
When comparing these six methods, we can see that none of these methods has absolute superiority in sensitivity, specificity, and False Discovery Rate (FDR)
Performed best in relation to sensitivity, its specificity was much lower than our proposed approach sliding window-based probability estimation (SWPE)
This paper proposes a novel approach for automatic detection of sleep spindles and introduced a method for comparing the performance of different detection methods
Summary
The sleep spindle is one of the few transient. A sleep spindle is a train of distinct waves with a frequency of 11–16 Hz (most commonly 12–14 Hz) (Berry et al 2012). The characteristics of sleep spindles, such as density, amplitude, or duration, vary substantially between individuals but are reasonably stable for each individual (Warby et al 2014; Tsanas and Clifford 2015). Sleep spindles play an important role in clinical research. Sleep spindles are believed to be associated with synaptic plasticity, memory consolidation, and long-term storage of memory representations (Warby et al 2014).
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