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

Bódizs, Róbert, Körmendi, János, Rigó, Péter and Lázár, Alpár Sándor (2009) The individual adjustment method of sleep spindle analysis:Methodological improvements and roots in the fingerprint paradigm. Journal of Neuroscience Methods, 178 (1). pp. 205-213. ISSN 0165-0270

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Abstract

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.

Item Type:	Article
Uncontrolled Keywords:	adolescent,adult,age factors,automatic data processing,brain mapping,circadian rhythm,electroencephalography,female,humans,individuality,male,middle aged,polysomnography,sleep,spectrum analysis,young adult
Faculty \ School:	Faculty of Medicine and Health Sciences > School of Health Sciences
UEA Research Groups:	Faculty of Medicine and Health Sciences > Research Groups > Dementia & Complexity in Later Life Faculty of Medicine and Health Sciences > Research Centres > Lifespan Health
Depositing User:	Pure Connector
Date Deposited:	07 Dec 2016 00:07
Last Modified:	19 Oct 2023 01:52
URI:	https://ueaeprints.uea.ac.uk/id/eprint/61620
DOI:	10.1016/j.jneumeth.2008.11.006

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