Application of Detrended Fluctuation Analysis of Electroencephalography during Sleep Onset Period.
- Author:
Doo Heum PARK
1
;
Chul Jin SHIN
Author Information
1. Department of Psychiatry, Konkuk University School of Medicine, Seoul, Korea.
- Publication Type:Original Article
- Keywords:
Sleep onset period;
Electroencephalography;
Detrended fluctuation analysis
- MeSH:
Brain;
Electroencephalography;
Female;
Humans;
Wakefulness
- From:Journal of the Korean Society of Biological Psychiatry
2012;19(1):58-62
- CountryRepublic of Korea
- Language:Korean
-
Abstract:
OBJECTIVES: Much is still unknown about the neurophysiological mechanisms or dynamics of the sleep onset process. Detrended fluctuation analysis (DFA) is a new tool for the analysis of electroencephalography (EEG) that may give us additional information about electrophysiological changes. The purpose of this study is to analyze long-range correlations of electroencephalographic signals by DFA and their changes in the sleep onset process. METHODS: Thirty channel EEG was recorded in 61 healthy subjects (male : female = 34 : 27, age = 27.2 +/- 3.0 years). The scaling exponents, alpha, were calculated by DFA and compared between four kinds of 30s sleep-wakefulness states such as wakefulness, transition period, early sleep, and late sleep (stage 1). These four states were selected by the distribution of alpha and theta waves in O1 and O2 electrodes. RESULTS: The scaling exponents, alpha, were significantly different in the four states during sleep onset periods, and also varied with the thirty leads. The interaction between the sleep states and the leads was significant. The means (+/- standard deviation) of alphas for the states were 0.94 (+/- 0.12), 0.98 (+/- 0.12), 1.10 (+/- 0.10), 1.07 (+/- 0.07) in the wakefulness, transitional period, early sleep and late sleep state respectively. The mean alpha of anterior fifteen leads was greater than that of posterior fifteen leads, and the two regions showed the different pattern of changes of the alpha during the sleep onset periods. CONCLUSIONS: The characteristic findings in the sleep onset period were the increasing pattern of scaling exponent of DFA, and the pattern was slightly but significantly different between fronto-temporal and parieto-occipital regions. It suggests that the long-range correlations of EEG have a tendency of increasing from wakefulness to early sleep, but anterior and posterior brain regions have different dynamical process. DFA, one of the nonlinear analytical methods for time series, may be a useful tool for the investigation of the sleep onset period.
