Abstract
A novel framework for joint detection of sleep spindles and K-complex events, two hallmarks of sleep stage S2, is proposed. Sleep electroencephalography (EEG) signals are split into oscillatory (spindles) and transient (K-complex) components. This decomposition is conveniently achieved by applying morphological component analysis (MCA) to a sparse representation of EEG segments obtained by the recently introduced discrete tunable Q-factor wavelet transform (TQWT). Tuning the Q-factor provides a convenient and elegant tool to naturally decompose the signal into an oscillatory and a transient component. The actual detection step relies on thresholding (i) the transient component to reveal K-complexes and (ii) the time-frequency representation of the oscillatory component to identify sleep spindles. Optimal thresholds are derived from ROC-like curves (sensitivity vs. FDR) on training sets and the performance of the method is assessed on test data sets. We assessed the performance of our method using full-night sleep EEG data we collected from 14 participants. In comparison to visual scoring (Expert 1), the proposed method detected spindles with a sensitivity of 83.18% and false discovery rate (FDR) of 39%, while K-complexes were detected with a sensitivity of 81.57% and an FDR of 29.54%. Similar performances were obtained when using a second expert as benchmark. In addition, when the TQWT and MCA steps were excluded from the pipeline the detection sensitivities dropped down to 70% for spindles and to 76.97% for K-complexes, while the FDR rose up to 43.62 and 49.09%, respectively. Finally, we also evaluated the performance of the proposed method on a set of publicly available sleep EEG recordings. Overall, the results we obtained suggest that the TQWT-MCA method may be a valuable alternative to existing spindle and K-complex detection methods. Paths for improvements and further validations with large-scale standard open-access benchmarking data sets are discussed.
Highlights
We spend about one third of our lives sleeping
The results of the proposed methodology are presented as follows: First, we provide the results of the training step (ROC-based identification of optimal thresholds), followed by the performance of the method on test sleep data (S2 and non-S2)
Sensitivity and false discovery rate (FDR) were computed using 210 30-s EEG S2 data segments for threshold values that varied in steps of 10 μV2 for spindles and 2 μV for K-complexes
Summary
We spend about one third of our lives sleeping. Luckily, and as might be expected of an efficient organism, the time we spend sleeping is not wasted idling. The analysis of sleep EEG signals helps us understand its neurophysiological basis and functional role. Macro and micro-structures are present in sleep signals at various temporal scales. Macro structure analysis often refers to sleep staging, i.e., the segmentation of brain signals into 20 s or 30 s-long periods that represent different sleep stages, each with distinct cerebral signatures. Micro structure analyses of brain signals during sleep consists of detecting short-lived microscopic events often considered to be hallmarks of specific sleep stages and of sleep-related cognitive processes, as well as potential signs of sleep anomalies. K-complexes and sleep spindles are among the most prominent micro-events studied in sleep studies, for their importance in sleep stage scoring (as they predominantly occur during S2 sleep stage), and for their importance in the diagnosis of sleep disorders and the exploration of the functional role of sleep
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