Abstract
SUMMARY This contribution provided comments onmethodological issues related to the auto-mated identification and characterizationof sleep spindles and their intracranialsources, and to the understanding of theirfunctional significance. Specific guide-lines were presented for the computer-based detection and analysis of spindlesand their intracranial sources, as wellas for related experimental and clinicalstudies. AUTHORCONTRIBUTIONS Periklis Y. Ktonas and Errikos-ChaimVentouras contributed to the conceptionanddesignofthework,aswellastodrafting and critically revising it, and toproviding final approval of the versionto be published. They both agree to beaccountable for all aspects of the work. REFERENCES Caporro,M.,Haneef,Z.,Yeh,H.J.,Lenartowicz,A., Buttinelli, C., Parvizi, J., et al. (2012).Functional MRI of sleep spindles and K-complexes. Clin. Neurophysiol . 123, 303–309.doi: 10.1016/j.clinph.2011.06.018Carvalho, D. Z., Gerhardt, G. J., Dellagustin, G.,de Santa-Helena, E. L., Lemke, N., Segal, A.Z., et al. (2014). Loss of sleep spindle fre-quency deceleration in Obstructive SleepApnea.
Highlights
Sleep spindles are short bursts of sleep EEG activity in the range of 11–15 Hz, reflecting central nervous system integrity and considered to promote sleep continuity, learning and memory consolidation processes. This contribution comments on the automated detection of sleep spindles and their intracranial sources, as well as on experimental and clinical studies for the characterization of spindles and their sources, and the study of their functional significance
In cases where not missing spindles is of paramount importance, as in sleep EEG records of patients with neurological or psychiatric disorders where there is a paucity of spindles, increasing TP performance (TPP) and decreasing FP performance (FPP) may be necessary
Other Distributed Source Model (DSM) methods, like dynamic SPM and standardized Low-Resolution Electromagnetic Tomography (LORETA), compute statistical scores indicating locations where activity would occur with low error probability, creating statistical parametric maps which can provide more focused loci of activity than LORETA
Summary
Automated detection of sleep spindles in the scalp EEG and estimation of their intracranial current sources: comments on techniques and on related experimental and clinical studies.
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