The individual adjustment method of sleep spindle analysis: Methodological improvements and roots in the fingerprint paradigm

Róbert Bódizs (Lead Author), János Körmendi, Péter Rigó, Alpár Sándor Lázár

Research output: Contribution to journalArticlepeer-review

98 Citations (Scopus)


Evidence supports the robustness and stability of individual differences in non-rapid eye movement (NREM) sleep electroencephalogram (EEG) spectra with a special emphasis on the 9-16 Hz range corresponding to sleep spindle activity. These differences cast doubt on the universal validity of sleep spindle analysis methods based on strict amplitude and frequency criteria or a set of templates of natural spindles. We aim to improve sleep spindle analysis by the individual adjustments of frequency and amplitude criteria, the use of a minimum set of a priori knowledge, and by clear dissections of slow- and fast sleep spindles as well as to transcend the concept of visual inspection as being the ultimate test of the method's validity. We defined spindles as those segments of the NREM sleep EEG which contribute to the two peak regions within the 9-16 Hz EEG spectra. These segments behaved as slow- and fast sleep spindles in terms of topography and sleep cycle effects, while age correlated negatively with the occurrence of fast type events only. Automatic detections covered 92.9% of visual spindle detections (A&VD). More than half of the automatic detections (58.41%) were exclusively automatic detections (EADs). The spectra of EAD correlated significantly and positively with the spectra of A&VD as well as with the average (AVG) spectra. However, both EAD and A&VD had higher individual-specific spindle spectra than AVG had. Results suggest that the individual adjustment method (IAM) detects EEG segments possessing the individual-specific spindle spectra with higher sensitivity than visual scoring does.

Original languageEnglish
Pages (from-to)205-213
Number of pages9
JournalJournal of Neuroscience Methods
Issue number1
Early online date18 Nov 2008
Publication statusPublished - 30 Mar 2009


  • Adolescent
  • Adult
  • Age Factors
  • Automatic Data Processing
  • Brain Mapping
  • Circadian Rhythm
  • Electroencephalography
  • Female
  • Humans
  • Individuality
  • Male
  • Middle Aged
  • Polysomnography
  • Sleep
  • Spectrum Analysis
  • Young Adult

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