Figure 2 - available via license: Creative Commons Attribution-NoDerivatives 4.0 International
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LME model predictions for spindle band power during SOs for each condition (analogous to condition averages). Averaged slow oscillations are superimposed in gray.
Source publication
Background: Oscillatory rhythms during sleep such as slow oscillations (SO) and spindles, and most importantly their coupling, are thought to underlie processes of memory consolidation. External slow oscillatory transcranial direct current stimulation (so-tDCS) with a frequency of 0.75 Hz has been shown to improve this coupling and memory consolida...
Contexts in source publication
Context 1
... predictions are depicted in Figure 2 and 3A -these are analogous to condition averages, and can be interpreted the same way. Parameter estimates and their corresponding significance are depicted in Supplementary Figure 1. ...
Context 2
... (gray dots in Fig. 3C) were analyzed in the same way. In the 12-15 Hz spindle band no differences between conditions were observed (all p> 0.1), however both eigen-and standardized frequency distributions differed to sham distribution in the 15-18 Hz band (D=0.571, p=0.001 and D=0.536, p=0.004, respectively), with no difference between the two (D=0.357, p=0.234). The length and direction of the resultant vectors of phase ( Fig. 3C and Table 3) indicate the general trend that both stimulation conditions, eigen-or standardized frequency, decrease the phase variance in comparison to sham, while pushing the mean phase value in the counterclockwise direction. Descriptively, standardized frequency ...
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Citations
... EEG data were recorded during 90-min afternoon naps as part of a larger study in which the effect of slow oscillatory transcranial direct current stimulation (so-tDCS) (Ladenbauer et al., 2021) was investigated. For the purposes of the current study, we selected baseline recordings from seven participants, together with so-tDCS recordings from two participants and one sham recording from one participant. ...
Study objectives
We aimed to build a tool which facilitates manual labeling of sleep slow oscillations (SOs) and evaluate the performance of traditional sleep SO detection algorithms on such a manually labeled data set. We sought to develop improved methods for SO detection.
Method
SOs in polysomnographic recordings acquired during nap time from ten older adults were manually labeled using a custom built graphical user interface tool. Three automatic SO detection algorithms previously used in the literature were evaluated on this data set. Additional machine learning and deep learning algorithms were trained on the manually labeled data set.
Results
Our custom built tool significantly decreased the time needed for manual labeling, allowing us to manually inspect 96,277 potential SO events. The three automatic SO detection algorithms showed relatively low accuracy (max. 61.08%), but results were qualitatively similar, with SO density and amplitude increasing with sleep depth. The machine learning and deep learning algorithms showed higher accuracy (best: 99.20%) while maintaining a low prediction time.
Conclusions
Accurate detection of SO events is important for investigating their role in memory consolidation. In this context, our tool and proposed methods can provide significant help in identifying these events.
