Deutsch
 
Benutzerhandbuch Datenschutzhinweis Impressum Kontakt
  DetailsucheBrowse

Datensatz

DATENSATZ AKTIONENEXPORT

Freigegeben

Zeitschriftenartikel

A comparison of two sleep spindle detection methods based on all night averages: individually adjusted vs. fixed frequencies

MPG-Autoren
/persons/resource/persons127940

Konrad,  Boris Nikolai
Dept. Clinical Research, Max Planck Institute of Psychiatry, Max Planck Society;

/persons/resource/persons80541

Steiger,  Axel
Dept. Clinical Research, Max Planck Institute of Psychiatry, Max Planck Society;

/persons/resource/persons80311

Dresler,  Martin
external;
Dept. Clinical Research, Max Planck Institute of Psychiatry, Max Planck Society;

Externe Ressourcen
Es sind keine Externen Ressourcen verfügbar
Volltexte (frei zugänglich)

fnhum-09-00052.pdf
(beliebiger Volltext), 4MB

Ergänzendes Material (frei zugänglich)
Es sind keine frei zugänglichen Ergänzenden Materialien verfügbar
Zitation

Ujma, P. P., Gombos, F., Genzel, L., Konrad, B. N., Simor, P., Steiger, A., et al. (2015). A comparison of two sleep spindle detection methods based on all night averages: individually adjusted vs. fixed frequencies. FRONTIERS IN HUMAN NEUROSCIENCE, 9: 52. doi:10.3389/fnhum.2015.00052.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-0028-F4EE-D
Zusammenfassung
Sleep spindles are frequently studied for their relationship with state and trait cognitive variables, and they are thought to play an important role in sleep-related memory consolidation. Due to their frequent occurrence in NREM sleep, the detection of sleep spindles is only feasible using automatic algorithms, of which a large number is available. We compared subject averages of the spindle parameters computed by a fixed frequency (FixF) (11-13 Hz for slow spindles, 13-15 Hz for fast spindles) automatic detection algorithm and the individual adjustment method (IAM), which uses individual frequency bands for sleep spindle detection. Fast spindle duration and amplitude are strongly correlated in the two algorithms, but there is little overlap in fast spindle density and slow spindle parameters in general. The agreement between fixed and manually determined sleep spindle frequencies is limited, especially in case of slow spindles. This is the most likely reason for the poor agreement between the two detection methods in case of slow spindle parameters. Our results suggest that while various algorithms may reliably detect fast spindles, a more sophisticated algorithm primed to individual spindle frequencies is necessary for the detection of slow spindles as well as individual variations in the number of spindles in general.