A MULTIVARIATE ANALYSIS OF HUMAN EEG DURING SLEEP

DOI Web Site PubMed

Bibliographic Information

Other Title
  • ヒトの睡眠脳波の多変量解析
  • ヒト ノ スイミン ノウハ ノ タヘンリョウ カイセキ

Search this article

Description

Natural sleep EEG was recorded monopolarly from the central cortex in 2 adult subjects and was analyzed into 10 frequency bands per 10 sec (Table 1). Means and standard deviations of integrated amplitudes were largest in delta bands and next largest in alpha bands. Correlation between the 2 subjects was fairly high both in mean integrated values (.927) and in standard deviations (.758) across 10 frequency bands.<br>The raw EEG data were classified visually per 10 sec segment into 9 EEG sleep patterns according to the criteria used by Koga and Fujisawa (Fig. 1), which was made prior to processing by the mathematical and statistical methods. Digree of similarity (or dissimilarity) between the 9 sleep EEG patterns was evaluated by Spearman's rank correlation and by a difference measure (formula 1 in text) in integrated values across 10 frequency bands without considering correlations between the bands (Table 2, Fig. 2). Multivariate analyses of variance applied to the data indicated significant differences between 9 visually classified EEG patterns and Mahalanobis' generalized distance functions were calculated and their squareroots are shown in Table 3, indicating statistical differences between the EEG patterns. Inter-subject correlation in these values was again substantial (.753).<br>Based on the correlation matrix in integrated values among 10 frequency bands (Table 4), the first three factors were extracted by Hotelling's method which explained in all about 80% of the total variance in both subjects (Table 5, Fig. 3). Factor I was highly associated with slow frequencies (less than 8cps) having little relation with alpha bands in both subjects who were different in faster frequency bands, although the correlation across the 10 frequency bands was still high (.915). Factor II was heavily loaded in alpha and faster frequency bands with almost no loadings in slower frequency bands. Factor III had a peculiar characteristic in that the 2 subjects showed the opposite profiles across the 10 bands (Fig. 3).<br>Each 10-sec set of 10-band integrated values was transformed into a single ‘component’ score for each of the three factors (components) extracted following Hotelling's principal component analysis (formula 5) and the distributions of these component scores were made as shown in Fig. 5 and Table 6. Mean component scores of the 9 sleep EEG patterns are plotted in Fig. 6 from which the first component (factor I) was assumed to reflect the depth of behavioral sleep. The second component or factor II was associated with the waking state (EEG sleep pattern 1) and slightly negatively related with paradoxical sleep (EEG sleep pattern 3-v). It was hard to interprete the third component or factor III in relation to sleep patterns.

Journal

Keywords

Details 詳細情報について

Report a problem

Back to top